Autonomous adaptive semiconductor manufacturing

ABSTRACT

An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. The network receives data from the tool system and from an external actor through the interaction manager. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Similarly, the one or more tools can be deployed recursively, in a bottom-up manner in which an individual autonomous tools is assembled in conjunction with other (disparate or alike) autonomous tools to form an autonomous group tool, which in turn can be assembled with other group tools to form a conglomerated autonomous tool system. Knowledge generated and accumulated in the autonomous learning system(s) associated with individual, group and conglomerated tools can be cast into semantic networks that can be employed for learning and driving tool goals based on context.

CROSS-REFERENCE TO RELATED APPLICATION

The subject application is related to co-pending, and commonly assigned,U.S. patent application Ser. No. ______, entitled “AUTONOMOUSBIOLOGICALLY BASED LEARNING TOOL,” filed on Mar. 8, 2008. The entiretyof this application is incorporated herein by reference.

BACKGROUND

Technological advances have lead to process-driven automated equipmentthat is increasingly complex. A tool system to accomplish a specificgoal or perform a specific, highly technical process can commonlyincorporate multiple functional elements to accomplish the goal orsuccessfully execute the process, and various sensors that collect datato monitor the operation of the equipment. Such automated equipment cangenerate a large volume of data. Data can include substantialinformation related to a product or service performed as a part of thespecific task, but it can also comprise sizable log information relatedto the execution of the process itself.

While modern electronic storage technologies can afford retainingconstantly increasing quantities of data, utilization of the accumulateddata remains far from optimal. Examination and interpretation ofcollected information generally requires human intervention, and whileadvances in computing power such as multiple-core processors, massivelyparallel platforms and processor grids, as well as advances in computingparadigms like object-oriented programming, modular code reuse, webbased applications and more recently quantum computing, the processingof the collected data remains to be a non-autonomous, staticprogrammatic enterprise wherein the data is operated upon. Moreimportantly, in non-autonomous data processing, the data fails to drivethe analysis process itself. As a consequence of such data processingparadigm, much of the rich relationships that can be present among datagenerated in automated equipment during a highly technical process canbe unnoticed unless a specific analysis is designed and focused on aspecific type of relationship. More importantly, emergent phenomena thatcan originate from multiple correlations among disparate data generatedby disparate units in the equipment, and that can determine optimalperformance of a complex automated tool or machine, can remainunnoticed.

Therefore, there is a need for automated equipment that is autonomousand can analyze data of a specific process, and on assets producedaccording to the specific process, consistently with a paradigm that isbased on relationships among the data, and wherein the analysis of thedata can be driven or affected by the data that surrounds the process orthe associated asset themselves through learning, much like in thefashion that the human brain operates—understanding of informationassociated with an processes or asset is affected by the informationitself, generally leading to learning and the ensuing revision ofanalysis goal(s), and analysis instrument(s) and approach(es) in orderto improve the understanding of the information and the quality of anassociated asset.

SUMMARY

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects of the invention. Thissummary is not an extensive overview of the invention. It is intended toneither identify key or critical elements of the invention nor delineatethe scope of the invention. Its sole purpose is to present some conceptsof the invention in a simplified form as a prelude to the more detaileddescription that is presented later.

An autonomous biologically based learning tool system and a method thatthe tool system employs for learning are provided. The autonomousbiologically based learning tool system includes (i) one or more toolsystems that are either individual systems or hierarchically deployedgroup and conglomerated systems, which perform a specific task, e.g., asemiconductor manufacturing task, or process, such as oxide etching orion implantation, and generates data that reflects the process and atool performance, (ii) an interaction manager that receives data andpackages the data for further utilization, and (iii) an autonomouslearning system based on biological principles of learning; the learningimplemented through spread activation of concepts in a set of semanticnetworks. The autonomous learning system comprises a functionalstructure that can be defined recursively from a group of threefunctional blocks: a memory platform, a processing platform, and aknowledge communication network, through which information iscommunicated among the memory and processing platforms, as well as thetool system and an external actor (e.g., a computer or a human agent).Memory platform includes a hierarchy of memories, including an episodicmemory to receive data impressions and associated learning instructions,a short term memory that is employed for knowledge development, and along term memory that stores knowledge, casting the knowledge intosemantic networks. Functional units in the processing platform operateon the information stored in the memory platform, facilitating learning.Such building blocks and associated functionality are inspired by thebiological structure and behavior of the human brain.

Learning is accomplished through concept activation in the definedsemantic networks, with activation thresholds dictated throughcombination of priorities associated with each concept. Prioritiesdepend on the type of concept that is manipulated; namely, a proceduralconcept possesses a priority based on activation and inhibitionenergies.

Individual, group or conglomerate autonomous tool systems exploit theknowledge that is generated and accumulated in the autonomous learningsystem, which leads to multiple improvements in the autonomousbiologically based learning tool as well as on assets fabricated by thevarious tool systems: (a) increased independence leading to lesser actorintervention (e.g., human direction and supervision) as time progresses,(b) increased production performance of outputs (e.g., output assets atleast partially finished) and ensuing higher quality of the outputs, (c)data assets that convey actionable information to actors (e.g.; statusof autonomous system degradation; better identification of root causesof failures; prediction of a set of system time-to-failure forindividual parts, tools, tool groups and conglomerated tool, as well asassociated time scales such as mean time between failures and mean timeto repair), and (c) enhanced performance over time-improved products orservices are delivered at a faster rate, with fewer resources consumed,and are produced with reduced tool down time.

To the accomplishment of the foregoing and related ends, the followingdescription and the annexed drawings set forth in detail certainillustrative aspects of the claimed subject matter. These aspects areindicative, however, of but a few of the various ways in which theprinciples of the claimed subject matter may be employed and the claimedsubject matter is intended to include all such aspects and theirequivalents. Other advantages and novel features of the claimed subjectmatter will become apparent from the following detailed description ofthe claimed subject matter when considered in conjunction with thedrawings.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a high level block diagram of an autonomousbiologically based learning tool.

FIG. 2 is a diagram that delineates contextual goal adaptation accordingto aspects described herein.

FIG. 3 illustrates a high level block diagram of an example autonomousbiologically based learning tool.

FIG. 4 is a diagram of an example tool system for semiconductormanufacturing that can exploit an autonomous biologically based learningsystem.

FIG. 5 illustrates a high level block diagram of example architecture ofautonomous biologically based learning system.

FIGS. 6A and 6B illustrate, respectively an example autobot componentand an example autobot architecture.

FIG. 7 illustrates an example architecture of a self-awareness componentof an autonomous biologically based learning system.

FIG. 8 is a diagram of example autobots that operate in an awarenessworking memory according to aspects described herein.

FIG. 9 illustrates an example embodiment of a self-conceptualizationcomponent of an autonomous biologically based learning system.

FIG. 10 illustrates and example embodiment of a self-optimizationcomponent in an autonomous biologically based learning system.

FIGS. 11A and 11B illustrate an example dependency graph with a singleprediction comparator and two recipe comparators, respectively,generated according to an aspect of the subject disclosure.

FIG. 12 illustrates a diagram of an example group deployment ofautonomous biologically based learning tool systems in accordance withaspects described herein.

FIG. 13 illustrates a diagram of a conglomerate deployment of autonomoustool systems according to aspects described herein.

FIG. 14 illustrates the modular and recursively-coupled characters ofautonomous tool systems described in the subject specification.

FIG. 15 illustrates an example system that assesses, and reports on, amulti-station process for asset generation in accordance with aspectsdescribed herein.

FIG. 16 is a block diagram of an example autonomous system which candistribute output assets that are autonomously generated by a toolconglomerate system in accordance with aspects set forth herein.

FIG. 17 illustrates an example of autonomously determined distributionsteps, from design to manufacturing and to marketing, for an asset(e.g., a finished product, a partially finished product, . . . ).

FIG. 18 presents a flowchart of an example method for biologically basedautonomous learning according to aspects described herein.

FIG. 19 presents a flowchart of an example method for adjusting asituation score of a concept according to an aspect described in thesubject specification.

FIG. 20 presents a flowchart of an example method for generatingknowledge in accordance with an aspect set forth herein.

FIG. 21 presents a flowchart of an example method for asset distributionaccording to aspects disclosed herein.

DETAILED DESCRIPTION

The subject innovation is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe present invention may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the present innovation.

As used in the subject specification, the terms “object,” “module,”“interface,” “component,” “system,” “platform,” “engine,” “unit,”“store,” and the like are intended to refer to a computer-related entityor an entity related to an operational machine with a specificfunctionality, the entity can be either hardware, a combination ofhardware and software, software, or software in execution. For example,a component may be, but is not limited to being, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, and/or a computer. By way of illustration, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. Also, these components canexecute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal).

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or”. That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

Referring to the drawings, FIG. 1 illustrates an example autonomousbiologically based learning system 100. An adaptive inference engine 110is coupled to a goal component 120. A wired or wireless communicationlink 115 couples such components. For a specific goal established orpursued by goal component 120, adaptive inference component 110 receivesan input 130 that can be employed to accomplish the goal and conveysoutput 140 that can represent or record aspects of the pursued oraccomplished goal. In addition, adaptive inference engine 110 canreceive data from a data store 150 through link 155, and can store dataor information in such data store, e.g., stored information can be aportion of output 140 that is conveyed through a wired or wireless link165. It should be appreciated that (i) input 130, output 140, and datain data store 150 (as well as the history of input, output, and data inthe data store) comprise a context for the operation of adaptiveinference engine 110, and (ii) a feedback of that context into theengine via links 115, 155, and 165 facilitates adaptation based oncontext. In particular, goal component 120 can exploit fed back contextto adapt a specific, initial goal and thus establish and pursue theadapted goal.

Input 130 can be regarded as extrinsic data or information, which caninclude (1) sounds, e.g., voice commands, environment noises or voices,alarms; (2) images captured by a static or mobile earth-based camera, oran airborne (e.g., plane, satellite) camera, wherein cameras can operatein multiple intervals of the radiation spectrum; (3) biometricindicators; (4) tokens such as batches of manufactured products, samplesof materials; data which can include instructions, records, results ofmeasurements; and so on. Output 140 can be substantially the same innature as input 130, and it can be regarded as intrinsic data. Input andoutput 140 can be received and conveyed, respectively, by input andoutput interfaces, e.g., cameras, input pads, media docks (e.g., USBports, IR wireless inputs), that can reside in adaptive inferencecomponent 110. As indicated above, input 130 and output 140 can be aportion of a context for adaptive inference engine 110. Additionally,adaptive inference component 110 can request input 130 as a result ofpursuing a goal.

Components in autonomous biologically based system 100 can be definedrecursively, which can confer the autonomous system 100 a substantialdegree of competent learning complexity with basic elementarycomponents.

Each link 115, 155, or 165 can include a communication interface thatcan facilitate manipulation of data or information to be transmitted orreceived; can utilize databases for data storage and data mining; andcan receive and convey information from and to an actor. Wiredembodiments of links 115, 155, or 165 can include a twisted-pair line, aT1/E1 phone line, an AC line, an optical fiber line, and correspondingcircuitry, whereas wireless embodiments can comprise an ultra-mobilewide band link, a long-term evolution link, or an IEEE 802.11 link, andassociated electronics. Regarding data store 150, although it isillustrated as a single element, it can be a distributed data warehouse,wherein set of data memories are deployed in disparate physical orlogical locations

In example system 100, the adaptive inference engine 110 and the goalcomponent 320 are illustrated as separate components, however, it shouldbe appreciated that one of such components can reside within the other.

Goal component 120 can belong to one or more disciplines (e.g., ascientific discipline, a commercial discipline, an artistic discipline,a cultural discipline, and so on) or enterprise sectors (e.g., a marketsector, an industry sector, a research sector, energy sector, publicpolicy sector, and so on). Additionally, as goals can typically bemultidisciplinary and focus on multiple markets, a goal component canestablish multiple disparate goals within one or more particulardisciplines or sectors. To pursue a goal, a goal component can comprisea functional component and a monitor component. Specific operations toaccomplish a goal are effected through the functional component(s),whereas conditions of variables related to the accomplishment of thegoal are determined by the monitor component. Additionally, thefunctional component(s) can determine a space of goals that can beaccomplished by the goal component 120. A space of goals comprisessubstantially all goals that can be attained with a specificfunctionality. It should be appreciated that, for such specificfunctionality afforded by a functional component, a contextualadaptation of a specific goal can adapt a first goal to a second goalwithin a space of goals. An initial goal within a space of goals can bedetermined by one or more actors; wherein an actor can be a machine or ahuman agent (e.g., an end user). It should be noted that an initial goalcan be a generic, high-level objective, as the adaptation inferenceengine 110 can drive goal component 120 towards a complex detailedobjective through goal drifting. Goals, goal components and goaladaptation are illustrated next.

FIG. 2 is a diagram 200 that delineates contextual goal adaptation. Agoal (e.g., goal 210 ₁, or goal 210 ₃) can typically be an abstractionthat is associated with the functionality of a goal component (e.g.,component 120). A goal can be a high level abstraction: “Save forretirement,” “secure a profit,” “be entertained,” “learn to cook,” “totravel to a locale,” “develop a database,” “manufacture a product,” andso on. Additionally, goals can be more specific refinements such as“save to retire early with an annual income in the range of$60,000-$80,000,” “travel from the United States to Japan in low season,with travel costs including housing not to exceed $5000,” or “reach ajob interview site to deliver a 35 minute presentation to a group ofassociates of the prospective employer.” Furthermore, a goal (e.g., 210₁) possesses an associated context (e.g., 220 ₁). As indicated above,goal component 120 coupled to adaptive inference engine 110 generally iscompatible with an established goal (e.g., goal 210 ₁, or goal 210 ₃).For instance, the goal “manufacture a product” (e.g., goal 210 ₁) canrely on a manufacturing tool system such as a molecular beam epitaxyreactor (an example goal component 120) that adopts standard or customspecifications to manufacture the product. During the accomplishment ofsuch a goal (e.g., goal 210 ₁), output 140 can include the manufacturedproduct. In addition, an adaptive inference component (e.g., component110) can adapt (e.g., adaptation 230 ₁) the “manufacture a product” goal(e.g., goal 210 ₁) based on context (e.g., context 220 ₁) like the onethat can be generated by tool system specifications or data gathered bya monitor component in the goal component. In particular, the initialhigh-level goal (e.g., goal 210 ₁) can be adapted to “manufacture asemiconductor device” (e.g., goal 210 ₂). As indicated above, a goalcomponent 120 can be composed of multiple functional components in orderto accomplish a goal. Additionally, goal component 120 can be modular,wherein goal sub-component can be incorporated as a goal is adapted. Asan example, a goal component that pursues the “manufacture a product”goal can comprise a multi-market evaluation and forecast component thatis coupled to a massively parallel, intelligent computing platform whichcan analyze market conditions in various markets in order to adapt(e.g., 230 ₁) the goal to “manufacture a multicore-processor thatutilizes molecular electronics components” (e.g., goal 210 _(N)). Itshould be noted that such an adaptation can involve a number ofintermediate adaptations 230 ₁-230 _(N-1), as well as intermediateadapted goals 210 ₂-210 _(N-1) wherein intermediated adaptation is basedon intermediate contexts 220 ₂-220 _(N) generated from a previouslypursued goals.

In another illustration of goal, goal component and goal adaptation, agoal can be to “purchase a DVD of movie A at store B,” the goalcomponent 120 can be a vehicle with a navigation system that comprisesan adaptive inference engine 110. (It should be noted that in thisillustration the adaptive inference engine 110 resides in the goalcomponent 120.) An actor (e.g., a vehicle operator) can enter or selectthe location of store B and goal component can generate directions toaccomplish the goal. In the instance that the adaptive inference engine110 receives input 130 that store B has ceased to carry in inventorymovie A (e.g., an RFID reader has updated an inventory database and anupdate message has been broadcasted to component 110) while the actor istraveling to the store, adaptive inference engine 110 can (i) requestadditional input 330 to identify a store C with movie A in stock, (ii)evaluate the resources available to the actor to reach store C, and(iii) assess the level of interest of the actor in accomplishing thegoal. Based on the modified context developed through input 130 asillustrated in (i)-(iii), goal component can receive an indication toadapt the goal “to purchase a DVD of movie A at store C.”

It should be appreciated that adaptive inference engine 110 canestablish sub-goals associated with a goal determined by goal component120. A sub-goal can facilitate accomplishing the goal by enablingadaptive inference engine to accomplish complementary task or to learnconcepts associated with the goal.

As a summary, autonomous biologically based system 100 is a goal-drivensystem with contextual goal-adaptation. It should be appreciated thatgoal adaptation based on received context introduces an additional layerof adaptation to the analysis of input information to generateactionable information output 140. The capabilities of (a) adapting theprocess of information or data analysis and (b) adapting an initial goalbased on context render the system massively adaptive or autonomous.

FIG. 3 illustrates a high level block diagram of an example autonomousbiologically based learning tool 300. In embodiment 300, the autonomouslearning system includes a tool system 310 that comprises a functionalcomponent 315 which confers the tool system its specific functionalityand can comprise a single functional tool component or a collection ofsubstantially identical or diverse functional tool components, and asensor component 325 that can probe several observable magnitudesrelated to a process performed by the tool, like a thermal treatment ofa semiconductor wafer, and generates assets 328 associated with theprocess. Collected assets 328, which include data assets such asproduction process data or test run data, can be conveyed to aninteraction component 330 which includes an adaptor component 335 thatcan serve as an interface to receive assets 328, an interaction manager345 which can process the received assets 328, and database(s) 355 thatcan store the received and processed data. Interaction component 330facilitates interaction of tool system 310 with autonomous biologicallybased learning system 360. Information associated with the datagenerated in the process performed by tool system 310 which can bereceived and incrementally supplied to autonomous learning system 360.

Autonomous biologically based learning system 360 includes a memoryplatform 365 that stores received information 358 (e.g., data, variablesand associated relationships, causal graphs, templates, and so on) whichcan be communicated via a knowledge network 375 to a processing platform385 that can operate on the received information, and can communicateback a processed information through the knowledge network 375 to thememory platform 365. The constituent components of autonomous learningsystem 360 can generally resemble biological aspects of the brain, inwhich a memory is networked with processing components to manipulateinformation and generate knowledge. Additionally, knowledge network 375can receive information from, and convey information to, interactioncomponent 330, which can communicate the information to tool system 310,or an actor 390 via interaction manager 345. As information 358 isreceived, stored, processed and conveyed by the autonomous learningsystem 360, multiples improvements can be effected in tool system 310and actors that rely on it. Namely, improvements include (a) theautonomous learning system 360 and tool system 310 become increasinglyindependent as time progresses, and require lesser actor intervention(e.g., human direction and supervision), (b) the autonomous systemimproves the quality of its outputs to actors (for example, betteridentification of root causes of failures, or prediction of systemfailure before occurrence thereof), and (c) the autonomous learningsystem 360 improves its performance over time—the autonomous system 360delivers improved results at a faster rate and with fewer resourcesconsumed.

Memory platform 365 comprises a hierarchy of functional memorycomponents, which can be configured to store knowledge (e.g.,information 358) received during initialization or configuration of toolsystem 310 (e.g., a priori knowledge). A priori knowledge can beconveyed as information input 358 through the interaction component 330.In addition, memory platform 365 can store (a) training data (e.g.,information input 358) employed to train the autonomous learning system360 after initialization/configuration of tool system 310, and (b)knowledge generated by the autonomous learning system 360; the knowledgecan be conveyed to tool system 310 or actor 390 through interactioncomponent 330, via interaction manager 345.

Information input 358 (e.g., data) supplied by an actor 390, e.g., ahuman agent, can comprise data identifying a variable associated with aprocess, a relationship between two or more variables, a causal graph(e.g., a dependency graph), or an episode information. Such informationcan facilitate to guide the autonomous biologically based system 360 ina learning process. Additionally, in one aspect, such information input358 can be deemed important by actor 390, and the importance can berelated to the relevance of the information to a specific processperformed by tool system 310. For instance, an operator (e.g., actor 390is a human agent) of an oxide etch system can determine that etch rateis critical to the outcome of the manufacturing process; thus, etch ratecan be an attribute communicated to autonomous learning system 360. Inanother aspect, information input 358 supplied by actor 390 can be ahint, whereby an indication to learn a particular relationship amongprocess variables is made. As an example, hint can convey a suggestionto learn the behavior of pressure in a deposition chamber in tool system310, within a specific deposition step, as a function of chamber volume,exhaust pressure and incoming gas flow. As another example, a hint canindicate to learn a detailed temporal relationship for a chamberpressure. Such example hints can activate one or more functionalprocessing units in the autonomous learning system that can learn thefunctional dependence of pressure on multiple process variables.Moreover, such hints can activate one or more functional units that canapply and compare a learnt functionality with respect to model orempirical functionalities available to actor 390.

A tool system 310, e.g., a semiconductor manufacturing tool, can becomplex and therefore disparate actors can specialize in manipulatingand operating the tool system through disparate types of specific,complete or incomplete knowledge. As an example, a human agent, e.g., atool engineer can know that different gases have different molecularweight and thus can produce different pressures, whereas a process/toolengineer can know how to convert a pressure reading resulting from afirst gas to an equivalent pressure resulting from a second gas; anelementary example of such knowledge can be to convert a pressurereading from a unit (e.g., Pa) to another (e.g., lb/in², or PSI). Anadditional type of general, more complex knowledge present in theautonomous biologically based learning system can be functionalrelationships between properties of a tool system (e.g., volume of achamber) and measurements performed in the tool system (e.g., measuredpressure in the chamber). For example, etch-engineers know that the etchrate is dependent on the temperature in the etch chamber. To allow forthe diversity of knowledge and the fact that such knowledge can beincomplete, an actor (e.g., a human agent such as an end-user) can guidean autonomous learning system 360 through multiple degrees of conveyedknowledge: (i) No knowledge specified. Actor delivers no guidance forthe autonomous learning system. (ii) Basic knowledge. Actor can convey avalid relationship between properties of a tool system and measurementsin the tool system; for instance, actor conveys a relationship (e.g.,relationship (κ_(E), T)) between an etch rate (κ_(E)) and processtemperature (T) without further detail. (iii) Basic knowledge withidentified output. Further to a relationship between a tool systemproperty and a tool system measurement, actor can provide specificoutput for a dependent variable in a relationship (e.g.,relationship(output(κ_(E)), T). (iv) Partial knowledge about arelationship. Actor knows the structure of a mathematical equation amonga tool system property and a measurement, as well as relevant dependentand independent variables (e.g., κ_(E)=k₁e^(−k2/T) without concretevalues for k₁ or k₂). The actor, however, can fail to know a precisevalue of one for more associated constants of the relationship. (v)Complete knowledge. Actor possesses a complete mathematical descriptionof a functional relationship. It should be noted that such guidance canbe incrementally provided over time, as the autonomous learning system360 evolves and attempts to learn tool functional relationshipsautonomously.

Knowledge network 375 is a knowledge bus that communicates information(e.g., data) or transfers power according to an established priority.The priority can be established by a pair of information source andinformation destination components or platforms. Additionally, prioritycan be based on the information being transmitted (e.g., thisinformation must be dispatched in real-time). It should be noted thatpriorities can be dynamic instead of static and change as a function oflearning development in the autonomous learning system 360, and in viewof one or more demands in the one or more components present in theautonomous biologically based learning tool 300—e.g., a problemsituation can be recognized and a communication can be warranted andeffected in response. Communication, and power transfer, via knowledgenetwork 375 can be effected over a wired link (e.g., a twisted pairlink, a T1/E1 phone line, an AC line, an optical fiber line) or awireless link (e.g., UMB, LTE, IEEE 802.11), and can occur amongcomponents (not shown) within a functional platform (e.g., memoryplatform 365 and processing platform 385) or among components indisparate platforms (e.g., a component in memory platform ofself-awareness communicating with another sub-component ofself-awareness) or the communication can be between components (e.g., acomponent of awareness communicates with a component inconceptualization).

Processing platform 385 comprises functional processing units thatoperate on information: Input information of a specific type (e.g.,specific data types such as a number, a sequence, a time sequence, afunction, a class, a causal graph, and so on) is received or retrievedand a computation is performed by a processing unit to generate outputinformation of a specific type. Output information can be conveyed toone or more components in memory platform 365 via knowledge network 375.In an aspect, the functional processing units can read and modify datastructures, or data type instance, stored in memory platform 335, andcan deposit new data structures therein. In another aspect, functionalprocessing units can provide adjustments to various numeric attributeslike suitability, importance, activation/inhibition energy, andcommunication priority. Each functional processing unit has a dynamicpriority, which determines a hierarchy for operating on information;higher priority units operate on data earlier than lower priority units.In case a functional processing unit that has operated on specificinformation fails to generate new knowledge (e.g., learn), likegenerating a ranking number or ranking function that distinguishes a badrun from a good run associated with operation of a tool system 310, thepriority associated with the functional processing unit can be lowered.Conversely, if new knowledge is generated, the processing unit'spriority is increased.

It should be appreciated that processing platform 385, throughprioritized functional processing units, emulates a human tendency toattempt a first operation in a specific situation (e.g., a specific datatype), if the operation generates new knowledge, the operation isexploited in a subsequent substantially identical situation. Conversely,when the first operation fails to produce new knowledge, a tendency toemploy the first operation to handle the situation is reduced and asecond operation is utilized (e.g., spread activation). If the secondoperation fails to generate new knowledge, its priority is reduced, anda third operation is employed. Processing platform 385 continues toemploy an operation until new knowledge is generated, and anotheroperation(s) acquire higher priority.

In an aspect, actor 390 can provide process recipe parameters,instructions (e.g., a temperature profile for an annealing cycle of anion implanted wafer, a shutter open/close sequence in a vapor depositionof a semiconductor, an energy of an ion beam in an ion implantationprocess, or an electric field magnitude in a sputtering deposition), aswell as initialization parameters for the autonomous learning system360. In another aspect, an actor can supply data associated withmaintenance of tool system 310. In yet another aspect, actor 390 cangenerate and provide results of a computer simulation of the processperformed by tool system 310. Results generated in such a simulation canbe employed as training data to train the autonomous biologically basedlearning system. Additionally, a simulation or an end-user can deliveroptimization data associated with a process to tool system 370.

Autonomous learning system 360 can be trained through one or moretraining cycles, each training cycle can be utilized to develop theautonomous biologically based learning tool 300 to (i) be able toperform a larger number of functions without external intervention; (ii)provide better response such as improved accuracy, or correctness, whendiagnosing root cause of manufacturing system health root causes; and(iii) increase performance such as faster response time, reduced memoryconsumption, or improved quality of product. Training data can besupplied to the autonomous learning system via adaptor component 335, incase training data is collected from data 328 associated with a processcalibration or standard run in tool system 310—such data can be deemedto be internal—or through interaction manager 345. When training data isretrieved from database(s) 365 (e.g., data related to externalmeasurements conducted through an external probe, or records of repairintervention in tool system 310); such training data can be deemedexternal. When training data is supplied by an actor, data is conveyedthrough interaction manager 345 and can be deemed external. A trainingcycle based on internal or external training data facilitates autonomouslearning system 360 to learn an expected behavior of tool system 310.

As indicated above, functional component 315 can comprise multiplefunctional tool components (not shown) associated with the tool specificsemiconductor manufacturing capabilities and that enable the tool to beused to (a) manufacture semiconductor substrates (e.g., wafers, flatpanels, liquid crystal displays (LCDs), and so forth), (b) conductepitaxial vapor depositiontion or non-epitaxial vapor deposition, (c)facilitate ion implantation or gas cluster ion infusion, (d) perform aplasma or non-plasma (dry or wet) an oxide etch treatment, (e) implementa lithographic process (e.g., photo-lithography, e-beam lithography,etc.), and so on. The tool system 310 can also be embodied in a furnace;an exposure tool for operation in a controlled electrochemicalenvironment; a planarization device; an electroplating system; a testdevice for optical, electrical, and thermal properties, which canincluded lifespan (through operation cycling) measurements; a metrologytool, a wafer cleaning machine, and the like.

In the process conducted by tool system 310, sensors and probescomprising sensor component 325 can collect data (e.g., data assets) ondifferent physical properties (e.g., pressure, temperature, humidity,mass density, deposition rate, layer thickness, surface roughness,crystalline orientation, doping concentration, etc.) as well asmechanical properties (valve aperture or valve angle, shutter on/offoperation, gas flux, substrate angular velocity, substrate orientation,and the like) through various transducers and techniques with varyingdegrees of complexity depending on the intended use of the gathereddata. Such techniques can include, but are not limiting to including,X-ray diffraction, transmission electron microscopy (TEM), scanningelectron microscopy (SEM), mass spectrometry, light-exposure assessment,magnetoelectric transport measurements, optical properties measurements,and so on. Additional data assets that are relevant to a product (e.g.,a semiconductor substrate) are development inspection (DI) criticaldimension (CD), and final inspection (FI) CI. It should be appreciatedthat probes can be external to tool system 310 and can be accessedthrough an interface component (not shown). For instance, such externalprobes can provide DI CI and FI CI. It should be appreciated that suchdata assets 328 effectively characterize output assets, or physicalproducts manufactured or fabricated by tool system 310.

In an aspect, data sources in sensor component 325 can be coupled toadaptor component 335, which can be configured to gather data assets 328in analog or digital form. Adaptor component 335 can facilitate data 368collected in a process run to be composed or decomposed according to theintended utilization of the data in autonomous learning system 310before the data is deposited into memory platform 365. Adaptors inadaptor component 335 can be associated with one or more sensors insensor component 325 and can read the one or more sensors at specificfrequencies, or in other specific conditions. An external data sourceadapter may have the ability to pull data as well as pass through datathat is pushed from outside the tool. For example, an MES/historicaldatabase adaptor knows how to consult an MES database to extractinformation for various autobots and package/deposit the data intoworking memory for one or more components of the autonomous system. Asan example, adaptor component 335 can gather wafer-level run data onewafer at a time as the tool processes the wafer. Then, adaptor component335 can consolidate individual runs in a batch to form “lot-level-data,”“maintenance-interval-data”, etc. Alternatively, if tool system 310outputs a single file (or computer product asset) for lot-level data,adaptor component 335 can extract wafer-level data, step-level data, andthe like. Furthermore, decomposed data elements can relate to one ormore components of tool system 300; e.g., variables and times at which apressure controller in sensor component 325 is operating. Subsequent toprocessing, or packaging, received data 328 as described above, adaptorcomponent 335 can store processed data in database(s) 355.

Database(s) 355 can include data originated in (i) tool system 370,through measurements performed by sensors in sensor component 325, (ii)a manufacturing execution system (MES) database or a historicaldatabase, or (iii) data generated in a computer simulation of toolsystem 310, e.g., a simulation of semiconductor wafer manufacturingperformed by actor 390. In an aspect, an MES is a system that canmeasure and control a manufacturing process, can track equipmentavailability and status, can control inventory, and can monitor foralarms.

It is to be appreciated that products, or product assets, fabricated bytool system 310 can be conveyed to actor 390 through interactioncomponent 330. It should be appreciated that product assets can beanalyzed by actor 390 and the resulting information, or data assets,conveyed to autonomous learning system 360. In another aspect,interaction component 330 can perform analysis of a product asset 328via adaptor component 335.

In addition it is to be noted that in embodiment 300 the interactioncomponent 340 and autonomous learning system 360 are externally deployedwith respect to tool system 310. Alternative deployment configurationsof autonomous biologically based learning tool 300 can be realized, suchas embedded deployment wherein interaction component 340 and autonomousbiologically based learning system 310 can reside within tool system370, in a single specific tool component; e.g., single embedded mode, orin a cluster of tool components; e.g., multiple embedded mode. Suchdeployment alternatives can be realized in a hierarchical manner,wherein an autonomous learning system supports a set of autonomouslearning tools that form a group tool, or a tool conglomerate. Suchcomplex configurations are discussed in detail below.

Next, an illustrative tool system 310 is discussed in connection withFIG. 4, and an example architecture for the autonomous biologicallybased learning system 360 is presented and discussed in detail withrespect to FIGS. 5-9.

FIG. 4 is a diagram of an example semiconductor manufacturing system 400that can exploit an autonomous biologically based learning system 360 tomonitor, analyze, and improve operation. In particular, system 400 is athermal development and coating system that illustrates a tool system310 discussed above in connection with FIG. 3. The system 400 includes aload/unload section 405, a process section 410, and an interface section415. In an aspect the load/unload section 405 has a cassette table 420on which cassettes 425 each storing a plurality of semiconductorsubstrates are loaded into and unloaded from the system 400. The processsection 410 has various single substrate processing units for processingsubstrates sequentially one by one. The interface section 415 canfacilitate access to multiple probes and sensors for quality assurance,process development, in situ root cause analysis. Collected data (e.g.,data 368) can be conveyed to the autonomous biologically based learningsystem, through an interface component.

In an aspect, process unit 410 comprises a first process unit group 430which possesses a cooling unit (COL) 435, an alignment unit (ALIM) 440,an adhesion unit (AD) 445, an extension unit (EXT) 450, two prebakingunits (PREBAKE) 455, and two postbaking units (POBAKE) 460, which arestacked sequentially from the bottom. Additionally, a second processunit group 465 includes a cooling unit (COL) 435, an extension-coolingunit (EXTCOL) 470, an extension unit (EXT) 475, a second cooling unit(COL) 435, two prebaking units (PREBAKE) 455 and two postbaking units(POBAKE) 460, Cooling unit (COL) 435 and the extension cooling unit(EXTCOL) 470 may be operated at low processing temperatures and arrangedat lower stages, and the prebaking unit (PREBAKE) 455, the postbakingunit (POBAKE) 460 and the adhesion unit (AD) 445 are operated at hightemperatures and arranged at the upper stages. With this arrangement,thermal interference between units can be reduced. Alternatively, theseunits can have alternative or additional arrangements. The prebakingunit (PREBAKE) 455, the postbaking unit (POBAKE) 460, and the adhesionunit (AD) 445 each comprise a heat treatment apparatus in whichsubstrates are heated to temperatures above room temperature. In anaspect, temperature and pressure data can be supplied to the autonomousbiologically based learning system 360 through interface component 340,from prebaking unit 455, postbaking unit 460, and adhesion unit 445.Rotational speed and positional data for a substrate can be conveyedfrom alignment unit 440.

FIG. 5 illustrates a high level block diagram of example architecture500 of an autonomous biologically based learning system. In embodiment500, autonomous learning system 360 comprises a hierarchy of functionalmemory components that include a long term memory (LTM) 510, a shortterm memory (STM) 520, and an episodic memory (EM) 530. Each of suchfunctional memory components can communicate through knowledge network375, which operates as described in discussed in connection with FIG. 3.In addition, autonomous learning system 360 can include an autobotcomponent 540 that includes functional processing units identified asautobots, with substantially the same characteristics as thosefunctional units described in connection with processing platform 385.It is to be noted that that autobot component 540 can be a part ofprocessing platform 385.

Furthermore, autonomous learning system 360 can comprise one or moreprimary functional units which include a self-awareness component 550, aself-conceptualization component 560, and a self-optimizing component570. A first feed forward (FF) loop 552 can act as a forward link andcan communicate data among self-awareness component 550 andself-conceptualization 560. In addition, a first feed back (FB) loop 558can act as a reverse link and can communicate data amongself-conceptualization component 570 and self-awareness component 550.Similarly, forward link and reverse link data communication amongself-conceptualization component 560 and self-optimization component 570can be accomplished, respectively, through a second FF loop 562 and asecond FB loop 568. It should be appreciated that in a FF link, data canbe transformed prior to communication to the component that receives thedata to further process it, whereas in a FB link a next data element canbe transformed by the component that receives the data prior to processit. For example, data transferred through FF link 552 can be transformedby self awareness component 550 prior to communication of the data toself-conceptualizing component 560. It should further be appreciatedthat FF links 552 and 562 can facilitate indirect communication of dataamong components 550 and component 570, whereas FB links 568 and 558 canfacilitate an indirect communication of data among components 570 and550. Additionally, data can be conveyed directly among components 550,360, and 370 through knowledge network 375.

Long term memory 510 can store knowledge supplied through interactioncomponent 330 during initialization or configuration of a tool system(e.g., a priori knowledge) to train the autonomous learning tool system300 after initialization/configuration. In addition, knowledge generatedby autonomous learning system 360 can be stored in long term memory 510.It should be appreciated that LTM 510 can be a part of a memory platform365 and thus can display substantially the same characteristics thereof.Long term memory 510 can generally comprise a knowledge base thatcontains information about tool system components (e.g., manufacturingcomponents, probe components, and so on), relationships, and procedures.At least a portion of the knowledge base can be a semantic network thatdescribes or classifies data types (for example as a sequence, anaverage, or a standard deviation), relationships among the data types,and procedures to transform a first set of data types into a second setof data types.

A knowledge base may contain knowledge elements, or concepts. In anaspect, each knowledge element can be associated with two numericattributes: a suitability (E) and an inertia (t) of a knowledge element,or concept; collectively such attributes determine a priority of aconcept. A well-defined function, e.g., a weighted sum, a geometricaverage, of these two numeric attributes can be a concept's situationscore (σ). For example, σ=ξ+ι. The suitability of a knowledge elementcan be defined as a relevance of the knowledge element (e.g., concept)to a tool system or a goal component situation at a specific time. In anaspect, a first element, or concept, with a higher suitability scorethan a second element can be more relevant to a current state of theautonomous learning system 360 and a current state of a tool system 310than the second element with a lower suitability score. The inertia of aknowledge element, or concept, can be defined as the difficultyassociated with utilization of the knowledge element. For example, a lowfirst value of inertia can be conferred to a number element, a list ofnumbers can be attributed a second inertia value higher than the firstvalue, a sequence of numbers can have a third value of inertia that ishigher than the second value, and a matrix of numbers can have a fourthvalue of inertia which can be higher than the third value. It is notedthat inertia can be applied to other knowledge or information structureslike graphs, tables in a database, audio files, video frames, codesnippets, code scripts, and so forth; the latter items can substantiallyall be a portion of input 130. The subject innovation provides for awell defined function of the suitability and the inertia that caninfluence the likelihood that a knowledge element is retrieved andapplied. Concepts that have the highest situational score are the mostlikely concepts to be rendered to short term memory 520 for processingby processing units.

Short term memory 520 is a temporary storage that can be utilized as aworking memory (e.g., a workspace or cache) or as a location wherecooperating/competing operations, or autobots, associated with specificalgorithms or procedures, can operate on data types. Data contained inSTM 520 can possess one or more data structures. Such data structures inSTM 520 can change as a result of data transformations effected byautobots and planner überbots (e.g., autobots dedicated to planning).The short term memory 305 can comprise data, learning instructionsprovided by the interaction manager 345, knowledge from the long termmemory 310, data provided and/or generated by one or more autobots orüberbots, and/or initialization/configuration commands provided by anactor 390. Short term memory 520 can track a state of one or moreautobots and/or überbots used to transform data stored therein.

Episodic memory 530 stores episodes which can include anactor-identified set of parameters and concepts which can be associatedwith a process. In an aspect, an episode can comprise extrinsic data orinput 130, and it can provide with a specific context to autonomouslearning system 100. It is noted that an episode can generally beassociated with a particular scenario identified or generated (e.g., bytool system 110, a goal component 120, or an autonomous learning system160) while pursuing a goal. An actor that identifies an episode can be ahuman agent, like a process engineer, a tool engineer, a field supportengineer, and so on, or it can be a machine. It should be appreciatedthat episodic memory 530 resembles a human episodic memory, whereinknowledge associated with particular scenario(s)—e.g., an episode—can bepresent and accessible without a recollection of the learning processthat resulted in the episode. Introduction, or definition, of an episodetypically is a part of a training cycle or substantially any extrinsicprovision of input, and it can lead to an attempt by the autonomousbiologically based learning system 360 to learn to characterize datapatterns, or input patterns, that can be present in data associated withthe episode. A characterized pattern of data associated with an episodecan be stored in episodic memory 530 in conjunction with the episode andan episode's name. The addition of an episode to episodic memory 530 canresult in a creation of an episode-specific autobot that can becomeactive when a set of parameters in a process conducted by a tool system310, or a generally a goal component 120, enter an operating range asdefined in the episode; the episode-specific autobot receives sufficientactivation energy when the first feature associated with a pursued goalor process is recognized. If the parameters meet the criteriaestablished through a received episode, the episode-specific autobotcompares the pattern of data in the episode with the current dataavailable. If the current situation (as defined by the recognizedpattern of data) of the tool system 310, or a goal component, matchesthe stored episode, an alarm is generated to ensure the tool maintenanceengineers can become aware of the situation and can take preventiveaction(s) to mitigate additional damage to functional component 315 orsensor component 325 or material utilized in a tool process.

Autobot component 540 comprises a library of autobots that perform aspecific operation on an input data type (e.g., a matrix, a vector, asequence, and so on). In an aspect, autobots exist in an autobotsemantic net, wherein each autobot can have an associated priority; apriority of an autobot is a function of its activation energy (E_(A))and its inhibition energy (E_(I)). Autobot component 540 is an organizedrepository of autobots that can include autobots for the self-awarenesscomponent 550, self-conceptualization component 560, self-optimizationcomponent 570, and additional autobots that can participate intransforming and passing data among components and among the variousmemory units. Specific operations that can be performed by an autobotcan include a sequence average; a sequence ordering; a scalar productamong a first and a second vector; a multiplication of a first matrixand a second matrix; a time sequence derivative with respect to time; asequence autocorrelation computation; a crosscorrelation operationbetween a first and a second sequence; a decomposition of a function ina complete set of basis functions; a wavelet decomposition of a timesequence numeric data stream, or a Fourier decomposition of a timesequence. It should be appreciated that additional operations can beperformed depending on input data; namely, feature extraction in animage, sound record, or biometric indicator, video frame compression,digitization of environment sounds or voice commands, and so on. Each ofthe operations performed by an autobot can be a named function thattransforms one or more input data types to produce one or more outputdata types. Each function for which there exists an autobot in autobotcomponent 540 can possess an element in LTM, so that überbots can makeautobot activation/inhibition energy decisions based on the total“attention span” and needs of the autonomous learning system 360.Analogously to the autonomous learning system 360, an autobot in autobotcomponent 540 can improve its performance over time. Improvements in anautobot can include better quality of produced results (e.g., outputs),better execution performance (e.g., shorter runtime, capability toperform larger computations, and the like), or enhanced scope of inputdomain for a particular autobot (e.g., inclusion of additional datatypes that the autobot can operate on).

Knowledge—concepts and data—stored in LTM 510, STM 520 and EM 530 can beemployed by primary functional units, which confer autonomousbiologically based learning system 360 a portion of its functionality.

Self-awareness component 550 can determine a level of tool systemdegradation between a first acceptable operating state of the toolsystem 310 and a subsequent state, at a later time, in which tool systemhas degraded. In an aspect, autonomous learning system 360 can receivedata that characterizes an acceptable operating state, and dataassociated with a product asset fabricated in such acceptable state;such data assets can be identified as canonical data. Autonomousbiologically based learning system 360 can process the canonical dataand the associated results (e.g., statistics about important parameters,observed drift in one or more parameters, predictive functions relatingtool parameters, and so on) can be stored by self-awareness component550 and employed for comparison to data supplied as information input358; e.g., production process data or test run data. If a differencebetween generated, learnt results of the canonical data and the deviceprocess run-data is small, then the manufacturing system degradation canbe considered to be low. Alternatively, if the difference between storedlearnt results of the canonical data and the sample process data islarge, then there can be a significant level of tool system (e.g.,semiconductor manufacturing system) degradation. A significant level ofdegradation can lead to a process, or goal, contextual adjustment.Degradation as described herein can be computed from a degradationvector (Q₁, Q₂, . . . , Q_(U)) where each component Q_(λ) (λ=1, 2, . . ., U) of the degradation vector is a different perspective of anavailable data set—e.g., Q₁ may be a multivariate mean, Q₂ theassociated multivariate deviation, Q₃ a set of wavelet coefficients fora particular variable in a process step, Q₄ may be the mean differencebetween a predicted pressure and measured pressure, etc. Normal trainingruns produce a specific set of values (e.g., a training data asset) foreach component, which can be compared with component Q₁-Q_(U) generatedwith run data (e.g., a run data asset) from each component. To assessdegradation, a suitable distance metric can be to employed to comparethe (e.g., Euclidean) distance of a run degradation vector from its“normal position” in {Q} space; the large such Euclidean distance, themore a tool system is said to be degraded. In addition, a second metriccan be to compute a cosine similarity metric among the two vectors.

Self-conceptualization component 560 can be configured to build anunderstanding of important tool system 310 relationships (e.g., one ormore tool behavior functions) and descriptions (e.g., statisticsregarding requested and measured parameters, influence of parameters ondegradation, etc.). It is to be appreciated that relationships anddescriptions are also data, or soft, assets. The understanding isestablished autonomously (e.g., by inference and contextual goaladaptation originated from input data; inference can be accomplished,for example, via multivariate regression or evolutionary programming,such as genetic algorithms) by autonomous learning system 360, orthrough an actor 390 (e.g., a human agent) supplied guidance.Self-conceptualization component 560 can construct a functionaldescription of a behavior of a single parameter of a tool system 310, orgenerally a goal component like component 120, such as pressure in adeposition chamber in a semiconductor manufacturing system as a functionof time during a specific deposition step. In addition,self-conceptualization component 560 can learn a behavior associatedwith a tool system, like a functional relationship of a dependentvariable on a specific set of input information 358. In an aspect,self-conceptualization component 560 can learn the behavior of pressurein a deposition chamber of a given volume, in the presence of a specificgas flow, a temperature, exhaust valve angle, time, and the like.Moreover, self-conceptualization component 560 can generate systemrelationships and properties that may be used for prediction purposes.Among learnt behaviors, self-conceptualization component can learnrelationships and descriptions that characterize a normal state. Suchnormal state typically is employed by autonomous learning system 360 asa reference state with respect to which variation in observer toolbehavior is compared.

Self-optimization component 570 can analyze a current health orperformance of an autonomous biologically based learning system 300based on the level of a tool system 310 deviation between predictedvalues (e.g., predictions based on functional dependence orrelationships learnt by self-conceptualization component 560 andmeasured values) in order to identify (a) a potential cause of failureof tool system 360, or (b) one or more sources of root cause of the toolsystem degradation based on information gathered by autonomous learningsystem 360. Self-optimizing component 570 can learn over time whetherautonomous learning system 360 initially incorrectly identifies anerroneous root cause for a failure, the learning system 300 allows forinput of maintenance logs or user guidance to correctly identify anactual root cause. In an aspect, the autonomous learning system 360updates a basis for its diagnosis utilizing Bayesian inference withlearning to improve future diagnosis accuracy. Alternatively,optimization plans can be adapted, and such adapted plans can be storedin an optimization case history for subsequent retrieval, adoption, andexecution. Moreover, a set of adaptations to a process conducted by toolsystem 310, or generally a goal pursued by a goal component 120, can beattained through the optimization plans. Self-optimization component 570can exploit data feedback (e.g., loop effected through links 565, 555,and 515) in order to develop an adaptation plan that can promote processor goal optimization.

In embodiment 500, autonomous biologically based learning system 360 canfurther comprise a planner component 580 and a system context component590. The hierarchy of functional memory components 510, 520, and 530,and the primary functional units 550, 560, and 570 can communicate withplanner component 580 and the system context component 590 throughknowledge network 375.

Planner component 580 can exploit, and comprise, higher level autobotsin autobot component 540. Such autobots can be identified as plannerüberbots, and can implement adjustments to various numeric attributeslike a suitability, an importance, an activation/inhibition energy, anda communication priority. Planner component 580 can implement a rigid,direct global strategy; for instance, by creating a set of plannerüberbots that can force specific data types, or data structures, to bemanipulated in short term memory 520 through specific knowledgeavailable in short term memory 505 and specific autobots. In an aspect,autobots created by planner component 580 can be deposited in autobotcomponent 540, and be utilized over the knowledge network 375.Alternatively, or in addition, planner component 580 can implement anindirect global strategy as a function of a current context of anautonomous learning system 360, a current condition of a tool system310, a content of short term memory 520 (which can include associatedautobots that can operate in the content), and a utilizationcost/benefit analysis of various autobots. It should be appreciated thatthe subject autonomous biologically based learning tool 300 can afforddynamic extension of planner components.

Planner component 580 can act as a regulatory component that can ensureprocess, or goal, adaptation in an autonomous biologically based tool300 does not result in degradation thereof. In an aspect, regulatoryfeatures can be implemented through a direct global strategy viacreation of regulatory überbots that infer operational conditions basedon planned process, or goal, adaptation. Such an inference can beeffected through a semantic network of data types on which theregulatory überbots act, and the inference can be supported orcomplemented by cost/benefit analysis. It should be appreciated thatplanner component 580 can preserve goals drifting within a specificregion of a space of goals that can mitigate specific damages to a goalcomponent, e.g., a tool system 310.

System context component 590 can capture the current competency of anautonomous biologically based learning tool 300 that exploits autonomouslearning system 360. System context component 590 can include a stateidentifier that comprises (i) a value associated with an internal degreeof competency (e.g., a degree of effectiveness of a tool system 310 inconducting a process (or pursuing a goal), a set of resources employedwhile conducting the process, a quality assessment of a final product orservice (or an outcome of a pursued goal), a time-to-delivery ofdevices, and so on), and (ii) a label, or identifier, to indicate thestate of the autonomous learning tool 300. For instance, the label canindicate states such as “initial state,” “training state,” “monitoringstate,” “learning state,” or “applying knowledge.” The degree ofcompetency can be characterized by a numerical value, or metric, in adetermined range. Further, the system context component 590 can includea summary of learning performed by the autonomous learning system 360over a specific time interval, as well as a summary of possible processor goal adaptations that can be implemented in view of the performedlearning.

FIG. 6A illustrates an example autobot component 540. Autobots 615 ₁-615_(N) represent a library of autobots and überbots, each with specificdynamics priority 625 ₁-625 _(N). Autobots 615 ₁-615 _(N) cancommunicate with a memory (e.g., a long term or short term memory, or anepisodic memory). As indicated supra, an autobot's priority, is adetermined by the autobot's activation energy and inhibition energy. Anautobot (e.g., autobot 615 ₁, or 615 _(N)) gains activation energy(through überbots) when data that can be processed by the autobot is inSTM. A weighted sum of an autobot (e.g., autobot 6152) activation energyand inhibition energy, e.g., Σ=w_(A)E_(A)+w₁E₁, can determine when theautobot can activate itself to perform its functional task: The autobotself-activate when Σ>ψ, where ψ is a predetermined, inbuilt threshold.It should be appreciated that the subject autonomous biologically basedlearning tool 300 can afford dynamic augmentation of autobots.

FIG. 6B illustrates an example architecture 650 of an autobot. Theautobot 660 can be substantially any of the autobots included in anautobot component 340. A functionality component 663 determines andexecutes at least a portion of an operation that autobot 660 can performon input data. Processor 666 can execute at least a portion of theoperation performed by the autobot 660. In an aspect, processor 666 canoperate as a co-processor of functionality component 663. Autobot 660can also comprise an internal memory 669 in which a set of results ofpreviously performed operations. In an aspect, internal memory operatesas a cache memory that stores input data associated with an operation,current and former values of E_(A) and E_(I), a log of the history ofoperation of the autobot, and so on. Internal memory 669 can alsofacilitate autobot 660 to learn how to improve quality of forthcomingresults when a specific type and quantity of error is fed back or backpropagated to the autobot 660. Therefore, autobot 660 can be trainedthrough a set of training cycles to manipulate specific input data in aspecific manner.

An autobot (e.g., autobot 660) can also be self-describing in that theautobot can specify (a) one or more types of input data the autobot canmanipulate or require, (b) a type of data the autobot can generate, and(c) one or more constraints on input and output information. In anaspect, interface 672 can facilitate autobot 660 to self-describe andthus express the autobot's availability and capability to überbots, inorder for the überbots to supply activation/inhibition energy to theautobots according to a specific tool scenario.

FIG. 7 illustrates example architecture 700 of a self-awarenesscomponent in an autonomous biologically based learning system.Self-awareness component 350 can determine a current level ofdegradation with respect to a learned normal state in a tool system(e.g., tool system 310). Degradation can arise from multiple sourcessuch as wear-and-tear or mechanical parts in the tool system; improperoperation or developmental operation to develop recipes (e.g., a dataasset) or processes that can force tool system to operate outside one ormore optimal ranges; improper customization of tool system; orinadequate adherence to maintenance schedules. Self-awareness component550 can be recursively assembled, or defined, through (i) a hierarchy ofmemories, e.g., awareness memories which can be part of memory platform365, (ii) functional operational units such as awareness autobots thatcan reside in an autobot component 540 and be a part of processingplatform 385, and (iii) a set of awareness planners. Based on the levelof degradation, autonomous learning system 360 can analyze availabledata assets 328 as well as information 358 to rank the possible faults.In an aspect, in response to an excessive level of degradation, e.g. atool system fault, an actor (e.g., a field engineer) can perform one ormore maintenance activities like cleaning a chamber, replacing a focusring, etc. In case of a successful repair of tool system, as confirmed,for example, by recovering degradation levels consistent withdegradation prior to the system fault, and associated symptoms (e.g.,data assets and patterns, relationships, and substantially any othertype of understanding extracted from such combination) that preceded themaintenance activities can be retained by autonomous learning system360. Thus, in forthcoming instances in which learned symptoms areidentified through new understanding autonomously gleaned from dataassets, and degradation analysis, a stored repair plan can be replayedreduce costs and improve mean time to repair (MTTR).

Awareness working memory (AWM) 710 is a S™ that can include a specialregion of memory identified as awareness sensory memory (ASM) 720 thatcan be utilized to store data, e.g., information input 358, that canoriginate in a sensor in sensor component 325 or in actor 390, can bepackaged by one or more adaptors in adaptor component 335, and can bereceived by knowledge network 375. Self-awareness component 550 can alsocomprise multiple special functionality autobots, which can reside inautobot component 540 and include awareness planner überbots (APs).

In addition, self-awareness component 550 can comprise an awarenessknowledge memory (AKM) 730 which is a part of a L™ and can includemultiple concepts—e.g., an attribute; an entity such as a class or acausal graph; a relationship, or a procedure—relevant to the operationof self-awareness component 550. In an aspect, a self-awarenesscomponent 550 for a semiconductor manufacturing tool can include domainspecific concepts like a step, a run, a batch, a maintenance-interval, awet-clean-cycle, etc., as well as general purpose concepts like anumber, a list, a sequence, a set, a matrix, a link, and so on. Suchconcepts can enter a higher level of abstraction; for instance, a waferrun can defined as an ordered sequence of steps where a step has bothrecipe parameter settings (e.g., desired values), and one or more stepmeasurements. Furthermore, AKM 730 can include functional relationshipsthat can link two or more concepts like an average, a standarddeviation, a range, a correlation, a principal component analysis (PCA),a multi-scale principal component analysis (MSPCA), a wavelet orsubstantially any basis function, etc. It should be noted that multiplefunctional relationships can be applicable, and hence related, to a sameconcept; for example, a list of numbers is mapped to a real numberinstance by the average, which is a (functional) relation and astandard-deviation relation, as well as a maximum relation, and soforth). When a relationship from one or more entities to another entityis a function or a functional (e.g., a function of a function), therecan be an associated procedure that can executed by an überbot in orderto effect the function. A precise definition of a concept can beexpressed in a suitable data schema definition language, such as UML,OMGL, etc. It should be further noticed that a content of AKM 730 can beaugmented dynamically at (tool system) runtime without shutting thesystem down.

Each concept in AKM 730, as any concept in a knowledge base as describedherein, can be associated with a suitability attribute and an inertiaattribute, leading to the concept's specific situation score. Initially,before the autonomous system is provided with data, the suitabilityvalue for all elements in AKM 730 is zero, but the inertia for allconcepts can be tool dependent and can be assigned by an actor, or basedon historical data (e.g., data in database(s) 355). In an aspect,inertia of a procedure that produces an average from a set of numberscan be substantially low (e.g., ι=1) because computation of an averagecan be regarded as a significantly simple operation that can beapplicable to substantially all situations involved collected data sets,or results from computer simulations. Similarly, maximize and minimizeprocedures, which transform a set of numbers, can be conferred asubstantially low inertia value. Alternatively, compute a range andcompute a standard deviation can be afforded higher inertia values(e.g., ι=2) because such knowledge elements are more difficult to apply,whereas calculate a PCA can display a higher level of inertia andcalculate a MSPCA can have a yet higher value of inertia.

A situation score can be employed to determine which concept(s) tocommunicate among from AKM 730 and AWM 710 (see below). Knowledgeelements, or concepts, that exceed a situation score threshold areeligible to be conveyed to AWM 710. Such concepts can be conveyed whenthere is sufficient available storage in AWM 710 to retain the conceptand there are no disparate concepts with a higher situation score thathave not been conveyed to AWM 710. A concept's suitability, and thus aconcept's situation score, in AWM 710 can decay as time progresses,which can allow new concepts with a higher suitability to enterawareness working memory 710 when one or more concepts already in memoryare no longer needed or are no longer applicable. It is noted that thelarger the concept's inertia the longer it takes the concept to both beconveyed to and be removed from AWM 710.

When a tool system state changes, e.g., a sputter target is replaced, anelectron beam gun is added, a deposition process is finished, an in situprobe is initiated, an annealing stage is completed, and so on,awareness planner 550 überbots can document which concepts (e.g.,knowledge elements) can be applied in the new state, and can increase asuitability value, and thus a situation score, of each such a concept inAKM 730. Similarly, the activation energy of autobots 615 ₁-615 _(N) canbe adjusted by überbots in order to reduce the activation energy ofspecific autobots, and to increase E_(A) for autobots that areappropriate to a new situation. The increment in suitability (andsituation score) can be spread by planner überbots to those concepts'first neighbors and then to second neighbors, and so forth. It should beappreciated that a neighbor of a first concept in AKM 730 can be asecond concept that resides, in a topological sense, within a specificdistance from the first concept according to a selected measure, e.g.number of hops, Euclidean distance, etc.) It is noted that the moredistant a second concept is from a first concept that received anoriginal increment in suitability, the smaller the second concept'sincrement in suitability. Thus, suitability (and situation score)increments present a dampened spread as a function of “conceptualdistance.”

In architecture 500, self-awareness component 550 comprises an awarenessschedule adapter (ASA) 760 which can be an extension of awarenessplanner component 750 and can request and effect changes in collectionextrinsic data or intrinsic data (e.g., via sensor component 325 throughinteraction component 330, via input 130, or via (feedback) link 155).In an aspect, awareness schedule adapter 760 can introduce data samplingfrequency adjustments—e.g., it can regulate a rate at which differentadaptors in adaptor component 335 can convey data to knowledge network375 (e.g., information input 358) intended for ASM 720. Moreover,awareness schedule adapter 760 can sample at low frequency, orsubstantially eliminate, collection of data associated with processvariables that are not involved in the description of normal patterns ofdata, or variables that fail to advance the accomplishment of a goal asinferred from data received in an adaptive inference engine. Conversely,ASA 760 can sample at higher frequency a set of variables extensivelyused in a normal pattern of data, or that can actively advance a goal.Furthermore, when the autonomous learning system 360 acknowledges achange of state tool system 310 (or a change in a situation associatedwith a specific goal) wherein data indicate that product quality orprocess reliability are gradually deviating from normal data patterns(or a goal drift is resulting in significant departure from an initialgoal in the space of goals), the autonomous learning system can request,via ASA 760, a more rapid sampling of data to collect a larger volume ofactionable information (e.g., input 130) that can effectively validatethe degradation and trigger an appropriate alarm accordingly. In anaspect, a goal component can display a goal drift summary to an actorthat entered an initial goal; e.g., a customer in an electronics storethat has substantially departed from an initial expenditure goal whenprocuring a home entertainment system can be displayed a log withchanges in a projected expense after budget adaptation; or a databasearchitect can be shown costs associated with memory space and associatedinfrastructure upon adaptation of a goal to optimize a data warehouse.

An actor 390 (e.g., a human agent) can train self-awareness component550 in multiple manners, which can include a definition of one or moreepisodes (including, for instance, illustrations of successfully adaptedgoals). A training of the autonomous learning system 360, throughself-awareness component 550, for an episode can occur as follows. Theactor 390 creates an episode and provides the episode with a uniquename. Data for the newly created episode can then be given to autonomouslearning system 360. The data can be data for a specific sensor during asingle specific operation step of a tool system, a set of parametersduring a single specific step, a single parameter average for a run,etc.

Alternatively, or additionally, more elementary guidance can be providedby actor 390. For example, a field support engineer can performpreventive tool maintenance (P_(M)) on tool system 310. P_(M) can beplanned and take place periodically, or it can be unplanned, orasynchronous. It should be appreciated that preventive tool maintenancecan be performed on the manufacturing system in response to a request bythe autonomous learning system 360, in response to routine preventivemaintenance, or in response to unscheduled maintenance. A time intervalelapses between consecutive PMs, during such a time interval one or moreprocesses (e.g., wafers/lots manufacturing) can take place in the toolsystem. Through data and product assets and associated information, suchas effected planner and unplanned maintenance, autonomous learningsystem can infer a “failure cycle.” Thus, the autonomous learning systemcan exploit asset(s) 328 to infer a mean time between failures (MTBF).Such inference is supported through a model of time-to-failure as afunction of critical data and product assets. Furthermore, autonomouslearning system 360 can develop models, through relationships amongdisparate assets received as information I/O 358 or through historicdata resulting from supervised training sessions delivered by an expertactor. It should be appreciate that an expert actor can be a disparateactor that interacts with a trained disparate autonomous learningsystem.

Actor 390 can guide the autonomous system by informing the system thatit can average wafer level run data and assess a drift in criticalparameters across P_(M) intervals. A more challenging exercise can alsobe performed by the autonomous system, wherein the actor 390 indicatesthrough a learning instruction to autonomous learning system 360 tolearn to characterize a pattern of data at the wafer average levelbefore each unplanned P_(M). Such an instruction can promote theautonomous learning system 360 to learn a pattern of data prior to anunplanned P_(M), and if a pattern of data can be identified by anawareness autobot, the self-awareness component 550 can learn such apattern as time evolves. During learning a pattern, awareness component550 can request assistance (or services) from self-conceptualizationcomponent 560 or awareness autobots that reside in autobot component540. When a pattern for the tool system is learned with a high degree ofconfidence (e.g. measured by a degree of reproducibility of the patternas reflected in coefficients of a PCA decomposition, a size of adominant cluster in a K-cluster algorithm, or a prediction of themagnitude of a first parameter as a function of a set of disparateparameters and time, and so forth), autonomous biologically basedlearning system 360 can create a reference episode associated with themalfunction that can lead to the need of tool maintenance so that analarm can be triggered prior to occurrence of the reference episode. Itis noted that awareness autobots, which can reside in autobot component540, can fail to characterize completely a data pattern for themalfunction reference episode, or substantially any specific situationthat can require unplanned maintenance, before it is necessary. Itshould be appreciated nonetheless that such a preventive healthmanagement of a tool system 310, which can include a deep behavioral andpredictive functional analysis, can be performed by autobots inself-conceptualization component 560.

FIG. 8 is a diagram 800 of autobots that can operate in an awarenessworking memory 520. Illustrated autobots—quantifier 815, expectationengine 825, surprise score generator 835, and summary generator 845—cancompose an awareness engine; a virtual emergent component, whoseemergent nature arises from the concerted operation of elementaryconstituents, e.g., autobots 815, 825, 835, and 845. It should beappreciated that the awareness engine is an example of how one or moreplanning überbots can use a collection of coordinated autobots toperform a sophisticated activity. The planning überbots employ thevarious autobots (e.g., average, standard deviation, PCA, wavelet,derivative, etc.) or the services of self-conceptualization component560 to characterize a pattern of the data received in an autonomousbiologically based learning system. Data for each step, run, lot, etc.run can be labeled by an external entity as being normal or abnormalduring training. Quantifier 815 can be employed by planning überbots toexploit normal data to learn a pattern of data for a prototypical,normal process. In addition, quantifier 815 can assess an unlabeled dataset (e.g., information input 358) that is deposited into ASM 720 andcompare the normal data pattern with a data pattern of unlabeled data.Expected patterns for normal data or equations to predict parameterswith normal data can be stored and manipulated through expectationengine 825. It should be noted that the pattern of unlabeled data candiffer from the normal data pattern in various ways, according tomultiple metrics; for instance, a threshold for a Hotelling T2 statistic(as applied to PCA and MS-PCA and derived from training runs) can beexceeded; an average of a data subset of the unlabeled data set candiffer by more than 3σ (or other predetermined deviation interval) fromthe average computed with normal, training run data; a drift of measuredparameters can be substantially different from that observed in the dataassociated with a normal run; and so forth. Summary generator 845 thusgenerates a vector of components for normal data, whereas surprise scoregenerator 835 can incorporate, and rank or weight substantially all suchdifferences in components of the vector and compute a net degradationsurprise score for the tool system that reflect a health condition ofthe tool system and reflect how far “away from normal” the tool systemis. It should be appreciated that discrepancies among a normal andunlabeled metric can vary as a function of time. Thus, throughcollection of an increasing amount of normal data, the autonomouslearning system 360 can learn various operational limits with greaterlevel of statistical confidence as time evolves and can adjustmanufacturing process recipes (e.g., a goal) accordingly Degradationcondition, as measured through a surprise score, for example, can bereported to an actor via summary generator 845.

FIG. 9 illustrates and example embodiment 900 of aself-conceptualization component of an autonomous biologically basedlearning system. A functionality of self-conceptualization component isto build an understanding of important semiconductor manufacturing toolrelationships and descriptions. Such an understanding can be employed toadjust a manufacturing process (e.g., a goal). This acquiredunderstanding is built autonomously or in conjunction with end-user(e.g., actor 390) supplied guidance. Similarly to the other primaryfunctional components 550 and 560, self-conceptualization component 570is assembled or defined recursively in terms of a hierarchy of memories,operational units, or autobots, and planners; such components cancommunicate a priority-enabled knowledge network.

Embodiment 900 illustrates a conceptualization knowledge memory (CKM)910 that includes concepts (e.g., attributes, entities, relationships,and procedures) necessary for operation of self-conceptualizationcomponent 570. Concepts in CKM 910 include (i) domain specific conceptssuch as a step, a run, a lot, a maintenance-interval, a wet-clean-cycle,a step-measurements, a wafer-measurements, a lot-measurements, alocation-on-wafer, a wafer-region, a wafer-center, a wafer-edge, afirst-wafer, a last-wafer, etc.; and (ii) general purpose, domainindependent concepts like a number, a constant (e.g., e, Z), a variable,a sequence, a time-sequence, a matrix, a time-matrix, afine-grained-behavior, a coarse-grained-behavior, etc.Self-conceptualization component also includes a vast array of generalpurpose functional relations such as add, subtract, multiply, divide,square, cube, power, exponential, log, sine, cosine, tangent, erf and soforth, as well as other domain specific functional relations that canpresent various levels of detail and reside in adaptiveconceptualization template memory (ACTM) 920.

ACTM 920 is an extension of CKM 910 that can hold functionalrelationships that are either completely or partially known to an actor(e.g., an end user) that interacts with a tool system 310 (e.g., asemiconductor manufacturing tool). It should be noted that while ACTM isa logical extension of CKM, autobots, planners, and other functionalcomponents are not affected by such separation, as the actual memorystorage can appear a single storage unit within self-conceptualizationcomponent 560. Self-conceptualization component 560 can also include aconceptualization goal memory (CGM) 930 which is an extension of aconceptualization working memory (CWM) 940. CGM 930 can facilitateautobots of a current goal, e.g., to learn f pressure, time, step); fora particular process step, learn a function f of pressure wherein thefunction depends on time. It should be noted that learning function frepresents a sub-goal that can facilitate accomplishing the goal ofmanufacturing a semiconductor device utilizing tool system 310.

Concepts in ACTM 920 also have a suitability numeric attribute and aninertia numeric attribute, which can lead to a situation score. A valueof inertia can indicate a likelihood of a concept to be learnt. Forexample, a higher inertia value for a matrix concept and a lower inertiafor a time-sequence concept can lead to a situation whereself-conceptualization component 560 can learn a functional behavior oftime-sequences rather than a functional behavior of data in a matrix.Similarly to self-awareness component 550, concepts with lower inertiaare more likely to be conveyed from CKM 910 to CWM 940.

Conceptual planners (CPs) provide activation energy to the variousautobots and provide situation energy to various concepts in CKM 910 andACTM 920, as a function of a current context, a current state of toolsystem 310 (or generally a goal component 120), a content of CWM 940, orcurrent autobot(s) active in CWM 940. It should be appreciated thatactivation energy and situation energy alterations can lead to goaladaptation based on the knowledge generated (e.g., based on learning) asa result of the altered semantic network for concepts in CWM 940 or CKM910—as inference by an adaptive inference engine can be based onpropagation aspects of concepts.

Contents of CTM 920 are concepts which can describe the knowledgediscussed above, and thus those concepts can have suitability andinertia numeric attributes. The contents of CTM 920 can be used byautobots to learn the functional behavior of the tool system 310(subject to the constraint that concepts with lower inertia are morelikely to be activated over concepts with higher inertia.). It is notnecessary for all guidance to have the same inertia; for instance, afirst complete function can be provided a lower inertia than a secondcomplete function even though both concepts represent completefunctions.

When partial knowledge like a partially-defined equation is uploaded inCWM 940, it can be completed, e.g., with existing knowledge—CPscoordinate autobots to employ available data to first identify valuesfor unknown coefficients. A set of ad hoc coefficients can thus completethe partially-defined equation concept into a complete function concept.The complete equation concept can then be utilized in a pre-builtfunctional-relation concept such as add, multiply, etc. Basic knowledgewith output (e.g., relationship(output(κ_(E)), T)) can facilitateautobots in CWM 940 to construct and evaluate various functionaldescriptions that involve data for κ_(E) and T in order to identify thebest function that can describe a relationship among κ_(E) and T.Alternatively, basic knowledge without output can facilitate autobots,with assistance of CPs, to specify a variable as an output, orindependent, variable and attempt to express it as a function of theremaining variables. When a good functional description is not found, analternative variable can be specified as an independent variable theprocess is iterated until it converges to an adequate functionalrelationship or autonomous learning system 360 indicates, for example toactor 390, that an adequate functional relationship is not found. Anidentified good functional relationship can be submitted to CKM 910 tobe utilized by autobots in autonomous learning system 360 with a levelof inertia that is assigned by the CPs. For instance, the assignedinertia can be a function of the mathematical complexity of theidentified relationship—a linear relationship among two variables can beassigned an inertia value that is lower than the assigned inertia to anon-linear relationship that involve multiple variables, parameters, andoperators (e.g., a gradient, a Laplacian, a partial derivative, and soon).

Conceptualization engine 945 can be a “virtual component” that canpresent coordinated activities of awareness autobots andconceptualization autobots. In an aspect, self-awareness component 550can feed forward (through FF loop 552) a group of variables (e.g.,variables in the group can be those that display good pairwisecorrelation properties) to self-conceptualization component 560.Forwarded information can facilitate self-conceptualization component560 to check CKM 910 and ACTM 920 for function relation templates. Theavailability of a template can allow an autobot of a conceptualizationlearner (CL), which can reside in the conceptualization engine 945, tomore quickly learn a functional behavior among variables in a forwardedgroup. It should be appreciated that learning such a functional behaviorcan be a sub-goal of a primary goal. A CL autobot with the assistance ofa CP autobot can also use autobots of a conceptualization validator(CV). CV autobots can evaluate a quality of proposed functionalrelationships (e.g., average error between a predicted value and ameasurement is within instrument resolution). A CL autobot canindependently learn a functional relationship either autonomously orthrough actor-supplied guidance; such actor supplied guidance can beregarded as extrinsic data. Functions learned by a CL can be fed back(e.g., via FB link 558) to self-awareness component 550 as a group ofvariables of interest. For example, after learning the functionκ_(E)=κ₀exp(−U/T), wherein κ₀ (e.g., an asymptotic etch rate) and U(e.g., an activation barrier) possess specific values known to the CL,self-conceptualization component 560 can feed back the guidance group(output(κ_(E), T) to self-awareness component 550. Such feed backcommunication can afford self-awareness component 550 to learn patternsabout such group of variables so that degradation with respect to thegroup of variables can be quickly recognized and, if necessary, an alarmgenerated (e.g., an alarm summary, an alarm recipient list verified) andtriggered. Memory 960 is a conceptualization episodic memory.

The following two aspects related to CL and CV should be noted. First,CL can include autobots that can simplify equations (e.g., throughsymbolic manipulation), which can facilitate to store a functionalrelationships as a succinct mathematical expression. As an example, therelationship P=((2+3)Φ)((1+0)÷θ) is simplified to P=3Φ÷θ, where P, Φ andθ indicate, respectively, a pressure, a flow and an exhaust valve angle.Second, CV can factor in the complexity of the structure of an equationwhen it determines a quality of the functional relationship—e.g., forparameters with substantially the same characteristics, like averageerror of predicted values versus measurements, a simpler equation can bepreferred instead of a more complicated equation (e.g., simpler equationcan have lower concept inertia).

Additionally, important FF 552 communication of information fromself-awareness component 550 to self-conceptualization component 560,and FB 558 communication from self-conceptualization component 560 toself-awareness component 550, can involve cooperation of awarenessautobots and conceptualization autobots to characterize a pattern ofdata for an episode. As discussed above in connection with FIG. 5, whenself-awareness component 550 fails to learn an episode,self-conceptualization component 560 can assist self-awareness component550 through provision of a set of relevant functional relationships. Forexample, characterization of an episode can require a fine-graineddescription of time dependence of a pressure in a stabilization step ina process run in a tool system 310. Self-conceptualization component 560can construct such a detailed (e.g., second by second) time dependenceof the pressure in the stabilization step. Thus, through FB loop 558,self-awareness component 550 can learn to characterize the pattern ofpressure during the stabilization step in a normal tool situation and tocompare the learnt pressure time dependence with a pattern of pressurein a specific episode data. As an illustration, presence of a spike in ameasured pressure prior to a stabilization step for data in an episode,and the absence of the spike in pressure data during normal tooloperation can be detected as a data pattern that identifies theoccurrence of the episode in an autonomous biologically based learningtool 300.

Similarly, a prediction of an unscheduled P_(M) can rely on knowledge oftemporal fluctuations of critical measurements of tool system data andthe availability of a set of predictive functions conveyed byself-conceptualization component 570. The predictive functions canassist a self-awareness component (e.g., component 550) to predict anemerging situation of an unplanned P_(M) in cases where the predictiondepends on projected values of a set of variables as a function of time.

FIG. 10 illustrates and example embodiment 1000 of a self-optimizationcomponent in an autonomous biologically based learning system. Asindicated above, self-optimization component functionality is to analyzethe current health (e.g., performance) of a tool system 310 and, basedon the results of the current health analysis, diagnose or ranksubstantially all potential causes for health deterioration of the toolsystem 310, and identify a root cause based on learning acquired byautonomous learning system 360. Analogously to the other primaryfunctional components 550 and 560, self-optimization component 570 isbuilt recursively from a hierarchy of memories that can belong to amemory platform 365, and autobots and planners which can be a part of aprocessing platform 385.

Optimization knowledge memory (OKM) 1010 contains concepts (e.g.,knowledge) related to diagnosis and optimization of the behavior of toolsystem 310. It should be appreciated that a behavior can include a goalor a sub-goal. Accordingly, OKM 1010 contains domain, or goal, specificconcepts such as step, step-data, run, run-data, lot, lot-data,P_(M)-time-interval, wet-clean-cycle, process-recipe, sensor,controller, etc. The latter concepts are associated with a tool system310 that manufactures semiconductor devices. In addition, OKM 1010comprises domain independent concepts, which can include a reading(e.g., readings from a pressure sensor in sensor component 325), asequence, a comparator, a case, a case-index, a case-parameter, a cause,an influence, a causal-dependency, an evidence, a causal-graph, etc.Furthermore, OKM 1010 can comprise a set of functional relations likecompare, propagate, rank, solve, etc. Such functional relations can beexploited by autobots, which can reside in autobot component 540 and canconfer OKM 1010 at least a portion of its functionality throughexecution of procedures. Concepts stored in OKM 1010 possess asuitability numeric attribute and an inertia numeric attribute, and asituation score attribute derived there from. The semantics ofsuitability, inertia and situation score is substantially the same asthat for self-awareness component 550 and self-conceptualizationcomponent 560. Therefore, if a run-data is provided with a lower inertiathan step-data, self-optimization component 570 planners (e.g.,überbots) are more likely to communicate the concept of run-data fromOMK 1010 to optimizing working memory (OWM) 1020. In turn, such inertiarelationship between run-data and step-data can increase the activationrate of optimization autobots that work with run related concepts.

It should be noted that through FF links 552 and 562, self-awarenesscomponent 550 and self-conceptualization component 560 can influence thesituation score of concepts stored on OKM 1010, and the activationenergy of optimization autobots through optimization planners (OPs),which can reside in optimization planner component 1050. It should beappreciated that concepts which are stored in OKM 1010, and areinfluenced through self-awareness component 550 andself-conceptualization component 560, can determine aspects of aspecific goal to be optimized as a function of a specific context. As anillustration, if self-awareness component 550 recognizes that a patternof data for a process step has degraded significantly, the situationscore of the associated step concept can be increased. Accordingly, OPscan then supply additional activation energy to optimizing autobotsrelated to the step concept in order to modify a set of steps executedduring a process (e.g., while pursuing a goal). Similarly, ifself-conceptualization component 560 identifies a new functionalrelationship among tool measurements for a product lot, FF informationreceived from self-conceptualization component 560 (via FF 562, forexample) self-optimization component 570 can increase (1) a situationscore of a lot concept and (2) an activation energy of an optimizationautobot with a functionality that relies on a lot concept; therefore,modifying aspects of the lot concept (e.g., number or type of wafers ina lot, cost of a lot, resources utilized in a lot, and so on).

Health assessment of a tool system 310 can be performed throughdiagnosing engine 825 as discussed next. It should be noted that ahealth assessment can be a sub-goal of a manufacturing process.Diagnosing engine 825 autonomously creates a dependency graph and allowsactor 390 to augment the dependency graph. (Such a dependency graph canbe regarded as extrinsic data or as intrinsic data.) The causal graphcan be conveyed incrementally, according to the dynamics of the processconducted by the tool system 310, and a diagnosis plan that can bedevised by the actor 390. For example, a causal graph can show that a“pressure” malfunction is caused by one of four causes: a depositionchamber has a leak, gas flow into the chamber is faulty, exhaust valveangle (which controls the magnitude of gas flow) is faulty, or apressure sensor is in error. Components of tool system 310 have a prioriprobabilities of failure (e.g., a chamber leak can occur withprobability 0.01, a gas flow can be faulty with probability 0.005, andso on). In addition, actor 390, or self-conceptualization component 560,can define a conditional dependency for pressure malfunction which canbe expressed as a conditional probability; e.g., probability of pressurebeing at fault given that the chamber has a leak can be p(P|leak).Generally, conditional probabilities causally relating sources of toolfailure can be provided by actor 390. It should be noted that autonomouslearning system 360 assumes that probability assignments defined byactor 390 can be approximate estimates, which in many cases can besignificantly different from a physical probability (e.g., actualprobability supported by observations). Examples of causal graphs arepresented and discussed next in connection with FIGS. 11A and 11B below.

Self-optimization component 570 can also comprise a prognostic component1060 which can generate a set of prognostics regarding performance oftool system 360 through information I/O 358 associated with the tool360. Such information can comprise quality of materials employed byfunctional component, physical properties of product assets 328 producedby tool system 360, such as index of refraction, optical absorptioncoefficient, or magnetotransport properties in cases product assets 328are doped with carriers, etc. Multiple techniques can be utilized byprognostics component 1060. The techniques comprise firstcharacterization techniques substantially the same as those techniquesthat can be employed by self-awareness component when processinginformation 358; namely, such as (i) frequency analysis utilizingFourier transforms, Gabor transforms, wavelet decomposition, non-linearfiltering based statistical techniques, spectral correlations; (ii)temporal analysis utilizing time dependent spectral properties (whichcan be measured by sensor component 325), non-linear signal processingtechniques such as Poincaré maps and Lyapunov spectrum techniques; (iii)real- or signal-space vector amplitude and angular fluctuation analysis;(iv) anomaly prediction techniques and so forth. Information, or dataassets generated through analysis (i), (ii), (iii) or (iv) can besupplemented with predictive techniques such as neural-networkinference, fuzzy logic, Bayes network propagation, evolutionaryalgorithms, like genetic algorithm, data fusion techniques, and so on.The combination of analytic and predictive techniques can be exploitedto facilitate optimization of tool system 310 via identification ofailing trends in specific assets, or properties, as probed by sensorcomponent 325, as well as information available in OKM 101, withsuitable corrective measures generated by optimization planner component1050, and optimization autobots that can reside in component 540.

FIG. 11A illustrates an example causal graph 900 generated byself-conceptualization component 530. A causal graph represents arelationship between dependent and independent variables of mathematicalfunction, or relationship, predicted by self-conceptualization component530. As an example, by accessing data for pressure (P), gas flow (Φ),and valve angle (θ), self-conceptualization component 530 can use one ormore mathematical techniques, such as curve fitting, linear regression,genetic algorithm, etc. to conceptualize, or learn, a predictivefunction 1110 for an output of interest or dependent variable, e.g.,pressure, as a function of data inputs or independent variables—gasflow, valve angle, temperature, humidity, etc. An example learntpredictive function 1110 can be the following relationship betweenpressure and the two input variables Φ, θ: P=2π(Φ/θ³). From such alearnt function, self-conceptualization component 530 autonomouslyconstructs the dependency graph 900.

To generate the dependency graph 1100 self-conceptualization component530 can proceed in two steps. (i) Comparator 1120 is introduced as aroot node that receives as input a single learnt function 1110. Afailure in comparator 1120 implies a failure in a tool (e.g., toolsystem 310) that employs a biologically based autonomous learningsystem. A comparator failure can be a Boolean value (e.g., “PASS/FAIL”1130) result which can be based on comparing a measured value of thepressure with a predicted value generated through learnt function 1110.Self-conceptualization component 530 flags a failure in comparator 1120when the average difference between predicted pressure values andcollected pressure data (e.g., as reported by a pressure sensor residingin sensor component 378) fails to remain within user-specifiedbounds—e.g., average difference is to remain within 5% of predictedpressure. A failure of comparator 1120 is made dependent on the outputof the predictive function 1110. Thus a comparator failure depends on(is influenced by) the failure of the pressure reading (P_(R) 1140);which can fail because a pressure sensor (P_(S) 1143) has failed or aphysical pressure (e.g., the physical quantity P_(P) 1146) has failed.Physical pressure P_(P) 1146 can fail because a pressure mechanism(P_(M) 1149) can fail. Thus the system autonomously creates thedependencies between P_(R) 1140 and {P_(S) 1143, P_(P) 1146} and betweenP_(P) 1140 and {P_(M) 1149}.

(ii) Dependent variables in learnt function 1110 are employed tocomplete the dependency graph as follows. Physical mechanism P_(M) 1149can fail when a gas-flow reading (Φ_(R) 1150) fails or a valve-anglereading (OR 1160) fails—dependent variables in learnt function 1110.Thus, self-conceptualization component 530 creates dependencies betweenP_(M) 1149 and {θ_(R) 1150, Φ_(R) 1160}. Substantially the sameprocessing, or reasoning, for a failure in a reading can be employed byself-conceptualization component 530 to create dependencies betweenΦ_(R) 1150 and {Φ_(S) 1153, Φ_(P) 1156} and between θ_(R) 1160 and{θ_(S) 1163, θ_(P) 1166}. Self-conceptualization component 530 then canadd the dependency between Φ_(P) 1156 and {Φ_(M) 1159} and between θ_(P)and {θ_(M)}. It is to be noted that the relationship between thephysical quantity (e.g., P_(P) 1146, Φ_(P) 1156, θ_(P) 1166) and theassociated mechanism (e.g., P_(M) 1149, Φ_(M) 1159, and θ_(M) 1169) isredundant and presented to enhance clarity—mechanism nodes (e.g., nodes1149, 1159, and 1169) can be removed, and their children made thechildren of the associated physical magnitude nodes (e.g., nodes 1146,1156, and 1169).

In a dependency graph such as dependency graph 900, leaf-level nodes arephysical points of failure; e.g., nodes 1140, 1143, 1146, and 1149;nodes 1140, 1153, 1156, and 1159; and 1160, 1163, 1166, and 1169. In anaspect, an actor (e.g., actor 390, which can be a user) can supply abiologically autonomous learning system with a priori probabilities forall physical points of failure. Such a priori probabilities can beobtained from manufacturing specifications for the component, fielddata, MTBF data, etc., or can be generated by simulation of theperformance of parts present in a manufacturing tool and involved in arelevant manufacturing processing. The actor can also supply conditionalprobabilities based on prior experience, judgment, field data, andpossible failure modes (e.g., the presence of a first failure caneliminate the possibility of a second failure, or the first failure canincrease the probability of occurrence of the second failure, etc.).Upon receiving a priori and conditional probabilities, for example viaan interaction component, such as component 340, the autonomous systemcan use Bayesian network propagation with learning to update theprobabilities based on actual failure data submitted to the autonomoussystem. Thus, in case the initial probabilities provided by the actorare erroneous, the autonomous system adjusts the probabilities as fielddata contradicts or supports a failure outcome; namely, a PASS or FAILresult of a comparator.

It should be noted that an actor (e.g., actor 390, which can be a user)can add dependencies to an autonomously generated dependency graph(e.g., dependency graph 900) rooted at mechanism failures. Such anaddition can be effected, for instance, through interaction manager 355.In an aspect, as an illustration, dependency graph 1100 is augmentedwith two nodes labeled P_(LEAK) 1170 and P_(ALT) 1173 that result in adependency of P_(M) 1149 on {Φ_(R) 1150, θ_(R) 1160, P_(LEAK) 1170, andP_(ALT) 1173}. It is to be appreciated that dependency graph 1100 can beaugmented with a deeper graph as well. Addition of node P_(LEAK) 1170informs the autonomous system, through self-conceptualization component530, that besides a failure of a gas flow reading or a valve anglereading, the pressure mechanism can also fail should a leak be presentin the tool. Node P_(ALT) 1173 is complementary to node 1170 in that itrepresents the likelihood that mechanisms alternative to a leak resultsin system failure. Upon addition of a node, or a deeper graph, the actoris to assign a priori probabilities for the node and associatedconditional probabilities describing the dependencies.

It should be appreciated that learnt functions can be more complex thanthe function P=F(Φ,θ) discussed above, and can include substantiallymore independent variables; however, causal graphs can be prepared insubstantially the same manner.

FIG. 11B is a diagram 1180 of an example learnt function dependencygraph with prediction and recipe comparators. In addition tolearnt-function comparators (e.g., comparator 1120), a biologicallybased autonomous learning system can generate one or more recipecomparators. A recipe comparator (e.g., comparator A 1195 _(A) orcomparator B 1195 _(B)) compares a set value of a recipe parameter witha corresponding average measure value, or reading, that arises from anassociated sensor in a tool system (e.g., tool system 370). In anaspect, given a collection of recipe parameters (e.g., θ 1185 _(A) orΦ1185 _(B)) that have an associated sensor and corresponding prescribedvalues, the autonomous system generates a recipe comparator for each setparameter. Similarly to a predicted function comparator, if the setrecipe value and the reading differ by a specific threshold which can bedetermined by an actor (e.g., actor 190), the recipe comparator signalsfailure. It should be noted that in diagram 1180 a recipe comparator forpressure is not generated since a process pressure is not set to aspecific value.

In order to identify a root cause, e.g., the physical point of failurewith the highest probability of failure, a biologically based autonomouslearning system can utilize a failure of one or more predictor or recipecomparators to rank all physical points of failure present in adependency graph. In an aspect, for a complete dependency graph with oneor more comparators, the biologically based autonomous learning systemcan use Bayesian inference to propagate the probabilities given thefailure signature of the comparators. Thus the system can compute theprobability of failure for a particular PASS/FAIL outcome (e.g., outcome1198 _(A) for comparator A 1195 _(A) or outcome 1198 _(B) for comparatorB 1195 _(B)) for each comparator. As an example, suppose that predictorcomparator 1120 and recipe comparator A 1195 _(A) fail whereascomparator B 1195 _(B) passes. The autonomous system can compute thefailure probability for each physical point of failure given thecomparator failures. (For example what is the probability of thepressure sensor failure given that comparator 1195 _(A) and comparator A1195 _(A) fail whereas comparator B 1195 _(B) passes). Each point offailure is then ordered from most likely to fail (highest computedprobability), or the most likely root cause, to least likely to fail(lowest computed probability). Identification of a root cause, which canbe deemed as actionable intelligence (e.g., output 140), can be conveyedto an actor via an interaction manager for further process; e.g., ordera new part, request a maintenance service (an actor communicates with orresides in the tool's manufacturer location), download a softwareupdate, schedule a new training session, and the like.

FIG. 12 illustrates a high level block diagram 1200 of an example groupdeployment of autonomous biologically based learning tool systems. Thegroup of autonomous tools systems 1220 ₁-1220 _(K) can be controlled byan autonomous biologically based learning tool 360 which receives(input) and conveys (output) information 358 to an interface 330 thatfacilitates an actor 390 to interact with the group of autonomous toolssystem 1220 ₁-1220 _(K) and with autonomous learning system 360.Individually, each of the autonomous tool systems 1220 ₁-1220 _(K) aresupported, or assisted, by associated autonomous learning systems 1250.Such learning system possesses substantially the same functionality oflearning system 360. It should be appreciated that in group 1210 each ofautonomous tools 1220 ₁-1220 _(K) can afford independent interaction,respectively, with associated local actors 390 ₁-390 _(K). Such actorpossesses substantially the same functionality than actor 390, asdiscussed in connection with FIG. 3 above. Additionally, an interactionwith autonomous tools 1220 ₁-1220 _(K) takes place in substantially thesame manner as in autonomous system 300, through an interactioncomponent 1240 and by providing and receiving tool-specific information(e.g., 1248 ₁-1248 _(K)) and assets, which both are typically toolssystem specific (e.g., assets 1250 ₁-1250 _(K)). In particular, itshould be appreciated that in group deployment 1212, each of actors 390₁-390 _(K) can monitor disparate aspects of operation its associatedsystem tool (e.g., system tool 1220 ₂). As an example, local actors 390₁-390 _(K) can establish a set of specific outputs (e.g., 1260 ₁-1260_(K)) to be critical. Such a determination can be based on historic dataor design (e.g., recipe for a process), or it can originate autonomouslythrough generated patterns, structures, relationships and the like. Inabsence of such a determination, group autonomous learning system 360assumes substantially all outputs (e.g., 1260 ₁-1260 _(K)) leading togroup output 1265 are critical.

In an aspect, autonomous learning system 360 can learn (through learningmechanisms described above in connection with system 300) expectedvalues for the critical output parameters during normal (e.g.,non-faulty) group tool 1200 operation. In an aspect, when measuredoutput 1265 deviates from an expected output, autonomous learning system360 can identify a performance metric of group 1200 performance asdegraded. It should be appreciated that the latter assessment canproceed in substantially the same manner as described in connection withsingle autonomous tool system 300; namely, through a self-awarenesscomponent in autonomous learning system 360. It is to be noted that eventhough autonomous group tool 1200 can present a degraded performance, asubset of autonomous tool system 12201-1220K can provide output that isnot degraded and meet individual expectation values for a predeterminedmetric.

In addition, similarly to the scenario of a single tool system (e.g.,tool system 310), autonomous learning system 360 can construct apredictive model for a critical output parameter as a function ofindividual tool related output parameters. It should be appreciated thatsuch output parameters can be collected through asset 328 input/output.It is to be noted that in group tool 1200, measurements of tool output(e.g., 1260 ₁-1260 _(K)) can be available to autonomous biologicallybased learning system 360 via sensor components residing in each of toolsystems 1220 ₁-1220 _(K), which can be accessed through deployedknowledge network extant in each autonomous learning system (e.g., 360,or 1250).

Furthermore, the autonomous system 360 can also construct a predictivemodel of group time-to-failure as a function of assets 328 of group1200; e.g., group input data, group outputs, group recipes, or groupmaintenance activities. In an aspect, to determine a grouptime-to-failure, autonomous learning system 360 can gather failure data,including time between detected (e.g., through a set of sensorcomponents) failures, associated assets 1250 ₁-1250 _(K), outputs12601-1260K, and maintenance activities for substantially all operationtools in the set of tools 12201-1220K. (It should be appreciated that asa consequence of prior failure assessments, specific tools (e.g., toolsystem 2 1220 ₁ and tool system K 1220 _(K)) in the set of tools (e.g.,tools 1220 ₁-1220 _(K)) in group 1200 can be out of operation.)Collected data can be autonomously analyzed (e.g., through a processingcomponent 385 in autonomous learning system 360) to learn a predictivefunction for time-to-failure as a function of the group assets (e.g.,inputs, recipes, . . . ), outputs, and maintenance activities. It shouldbe appreciated that the group time-to-failure model constructed from thecollected data can readily display substantially dominant factors thatimpact performance of group tool 1200.

In an aspect, time-to-failure models constructed for individualcomponents of tool systems (e.g., 1220 ₁-1220 _(K)) in group tool 1200can be employed by actor 390 (e.g., a group level controller) tooptimize part inventory and optimize maintenance scheduling. It shouldbe appreciated that such optimization can be conducted, at least inpart, by autonomous system 360. For example, the autonomous systemaccesses the MES (or ERP) system to identify the number of availableparts. When a set of parts that provide functionality to tool systems1220 ₁-1220 _(K) (e.g., parts in one or more of components within afunctional component like a component 315 in system 310), and can beexpected to be necessary (e.g., for replacement) within a specific timeperiod Δτ, exceeds an available supply in stock, additional parts can beordered. Alternatively, or in addition, when parts are available, anexpected schedule of necessary parts can be analyzed to determine anoptimal, or adequate, time to place a new order.

It should be appreciated that maintenance schedules can be reassessedand optimized during a necessary, previously scheduled, maintenanceactivity, in order to exploit an opportunity available to autonomoussystem 360 to analyze parts and identify parts that can fail in asubstantially short period of time. It should further be appreciatedthat a group or individual time-to-failure schedule can be complemented,autonomously in an aspect, with additional information such as cost ofparts, time to replace parts, and so forth, to determine whetherreplacement of a part during a current maintenance cycle is beneficialwith respect to the replacement of the part in a forthcoming scheduledmaintenance cycle. It is noted that autonomous system 360 can also takeas input various costs associated with the operation of group tool 1200in order to compute a cost per output product (e.g., a wafer, a car, acomputer, etc.) for the group, and a total cost to produce a specificorder during operation of the group tool 1200. After building a model ofcost as a function of individual tool assets 1250 ₁-1250 _(K) (e.g.,recipes), outputs 1260 ₁-1260 _(K), and maintenance activities,autonomous system 360 can rank individual tool systems 1220 ₁-1220 _(K)in increasing order of operation cost. A combined cost data asset can beutilized construct a predictive model of cost versus assets, outputs,and maintenance activities associated with the individual toolsystems—for example, such an assessment can identify operational assetsand variables that affect substantially an operation or maintenance costfor the group tool. In an aspect, autonomous system 360 can utilizeavailable historic data assets to redesign a production line, orequipment configuration in a floor plant, in order to minimize costs. Inaddition, during such an optimization process, autonomous system 360 canrely on shutdown of various tool systems in order to exploit alternativepatterns of operation. Furthermore, autonomous system 360 can utilizecost-benefit analysis to determine a set of trade-off scenarios in whichproduction of specific output proceeds without output for specific,highly costly tool systems.

Tools system 1220 ₁-1220 _(K) can be substantially the same, or can bedisparate (e.g., tool systems 1220 ₁-1220 ₃ are steppers, tool 1220 _(j)is a stepper, and 1220K-4-1220K are turbomolecular vacuum pumps).Typically, a central difference amongst homogeneous (e.g., tool systemsare alike) and heterogeneous (e.g., tools are disparate) can lie in thatinput and output measurements (e.g., measurement assets) are distinct.For example, a critical output of interest for tool group 1200 can be D1CD uniformity, but a coating system that is part of the group tool 1200can fail to provide such output measurements. Accordingly, autonomoussystem 360 can construct a model for expressing a tool group's outputsas a function of individual tool (e.g., 1220 ₁-1220 _(K)) outputs. Thus,when a group performance appears degraded, individual performancesassociated with individual tools can be analyzed to isolate a tool thathas the largest weight in causing the performance degradation.

FIG. 13 illustrates a diagram of a conglomerate deployment of autonomoustool systems. Conglomerate system 1310 comprises a set of autonomoustool conglomerates 1320 ₁-1320 _(Q). Each of the tool conglomerates cancomprise homogeneous or heterogeneous groups of autonomous tools, e.g.,a set of disparate autonomous tools groups which can comprise amautonomous fabrication facility (not shown), or a set of disparateautonomous fabrication facilities. It should be appreciated thatautonomous conglomerates 1320 ₁-1320 _(Q) can typically be located indisparate geographic locations (for example, a conglomerate canrepresent a car assembly line that mounts vehicles with parts fabricatedin a disparate location). Similarly, groups of autonomous tools in afactory can be deployed in disparate locations within a plant in viewthat a manufacturing process can comprise multiple steps. Accordingly,product output chain 1365 can facilitate providing disparate autonomoustool conglomerates 1320 ₁-1320 _(Q) with partially manufactured orprocessed or analyzed products; such features indicated withbidirectional arrows 1360 ₁-1360 _(Q) which represent output/inputassociated with conglomerates 1320 ₁-1320 _(Q).

Conglomerate system 1310 can be autonomously supported by an autonomouslearning system comprising an interaction component 340, an actor 390,and an autonomous learning system 360. In an aspect, autonomous supportcan be directed toward improving an overall fabrication effectiveness(OFE) metric of output assets (e.g., output 1365 or 1265). In addition,each of the autonomous tool conglomerates 1320 ₁-1320 _(Q) can be inturn autonomously supported by an interaction component 1330, and anautonomous learning system 1340. Interface component 1330 facilitatesinteraction between autonomous learning system 1340 and actors 390 ₁-390_(Q). Functionality of each of such components is substantially the sameas the functionality of respective component described above inconnection with system 360 and system 1200. Information 1348, (I=1, 2, .. . , Q) communicated among interaction component 1330 and autonomoussystem 1340 is associated with the respective autonomous toolconglomerate I 1320 _(I). Similarly, assets 1350 _(I) conveyed to andreceived from an autonomous tool conglomerate I 1320 _(I) are specificthereof.

To address performance in an autonomous tool conglomerate 1310 ₁-1310_(Q), the multi-step characteristics of a fabrication process can beincorporated through a performance tag that identifies productsutilizing a composite conglomerate index C_(α), wherein the index αindicates a specific tool group within conglomerate C (e.g., autonomousconglomerate 1320 _(Q)), and a run index (R); thus, a product quality,or performance metric associated with a specific product is identifiedvia a label (C_(α);R), which can be termed “group-layer output.” Suchlabel facilitates identifying each autonomous operation group as anindividual component C_(α). Therefore, autonomous system 360 can mapquality and performance metrics as a function of fabricationconglomerate (e.g., autonomous tool conglomerate 1310 ₂) and as afunction of tool group within each fabrication conglomerate. The latterfacilitates root-cause analysis of poor performance or quality, by firstidentifying a conglomerate (e.g., a fabrication facility) andsubsequently performing the analysis for the tool associated with theassessed degradation. It should be appreciated that index C_(α) toaccount for the fact that output assets generated in an autonomoussystem comprised of multiple conglomerate tools can be transported froma first conglomerate (N) to a second conglomerate (N′). Thus, thecomposite symbol for tracking performance associated with a transfer ofassets (e.g., as a part of a multi-step fabrication process) can readC_(α:N→N′).

Performance of an autonomous tool conglomerate can be performed as afunction of product yield. Such yield is utilized to rank disparateconglomerates. In an aspect, autonomous learning system 360 can developa model for yield based at least in part on output assets from eachautonomous tool, or autonomous group tool. For example, for tools, orgroup of tools, employed in semiconductor manufacturing, yield can beexpressed as a function of a wafer thickness, a device uniformity, animpurity (e.g., extrinsic and intrinsic dopant concentration)concentration, a DI CD, an FI CD, and so on. Moreover, other yieldmetrics can be utilized to determine a model for a yield, specially inan autonomous learning systems comprising tool conglomerates systems(e.g., 1320 ₁-1320 _(Q)) wherein output assets can be transported amongconglomerates: an overall equipment efficiency (OEE), a cycle timeefficiency, an on-time-delivery rate, a capacity utilization rate, arework rate, a mechanical line yield, a probe yield and final testyield, an asset production volume, a startup or ramp-up performancerate, etc. It is to be noted that an autonomous system that supportsoperation of a set of autonomous tool conglomerates can autonomouslyidentify relationships amongst yield metrics in order to redesignprocesses or communicate with actors 390 ₁-390 _(Q) with respect toadjustments in connection to said yield metrics.

The yield function mentioned supra can be analyzed through a combinationof static and dynamic analysis (e.g., simulation) to rank group layeroutputs according to degree of influence, or weight, in leading to aspecific yield. It is to be noted that ranking tools, group of tools, orconglomerates, at a group-layer-output level based at least in part oninfluence in affecting asset output, or yield, can afford a group orconglomerate autonomous learning system 360 to autonomously identify,through autonomous systems associated with each of the tools in a groupor group in a conglomerate, whether a specific tool can be isolated as adominant tool in yield deterioration. When such a tool is located, thegroup or conglomerate level autonomous system 360 can issue an alarm toa maintenance department with information regarding ranking the faultsthat can be candidates for performance degradation.

In addition, yield for the lowest ranking autonomous tool conglomeratecan be employed to identify the group layer outputs of the tool groupthat is dominant in its impact on yield. The time-to-failure for suchtool-group can be compared with substantially the same tool groups indisparate autonomous conglomerates in order to identify cause(s) of poorperformance. Furthermore, an autonomous tool conglomerate system rankstools within a specific tool group in disparate tool conglomerates. Itis to be noted that an autonomous learning system that supports andanalyzes a group of autonomous tool conglomerates (e.g., 1320 ₁-1320_(Q)) can rank each of the conglomerates according to inferredtime-to-failure for each conglomerate. Since time-to-failure can changeover operational time intervals in view of, e.g., input/output asset(e.g., asset 358) load, a database with time-to-failure projection canbe updated at specified periods of time (e.g., weekly, monthly,quarterly, or yearly).

Further yet, when an individual tool that is primarily responsible for agroup tool's poor performance (e.g., the tool ranks the lowest inperformance within a group tool, such as a tool that most frequentlyfails to output assets with specified target properties of quality likeuniform doping concentration or uniform surface reflection coefficient)is identified, an autonomous system associated with the lowestperforming tool, or with the conglomerate system that includes such poorperforming tool, can analyze the tool's outputs to identify thoseoutputs that most significantly affect the output of the lowestperforming group. For example, a tool in a tool group or conglomeratethat outputs assets with low uniformity as illustrates above, can leadto a substantial percentage (e.g., 60%) of tool groups uniformityvariation (for example, variation in uniformity change of surfacereflectivity of an optical display due to uniformity issues on surfacereflectivity of coatings on otherwise high-quality displays). To thatend, in an aspect, for each output in the group the tool autonomoussystem constructs a function that expresses tool output as a function oftool assets (e.g., inputs, recipes, and process parameters, tooloperator or actor, and so on). This model is then analyzed to identifythe dominant factors in poor performance. It is to be noted that anautonomous system can identify best performing tools in a group tool andanalyze causes that result in the tool having the best performance;e.g., the vacuum level of the tool during operation is consistentlylower than vacuum level of disparate tools in the group tool, or duringepitaxial deposition a wafer in the best performing tool rotates at alower speed than in disparate tool carrying out a deposition, thus thetool consistently achieves greater device quality. Such factors inhighest ranking and lowest ranking tools can be compared with sameparameters in other tools in conglomerate system. In case the comparisonindicates that the factors identified as the root causes of highest andlowest ranking performance appear to be substantially the samethroughout the tool conglomerate system, then a new model can bedeveloped and alternative root causes can be identified. Such iterative,autonomous processes of model development and validation can continueuntil root causes are identified and best practices are emulated (e.g.,a coating recipe utilized in tool conglomerate 1320 _(P) is adopted insubstantially all tool conglomerates in view that it increases outputasset performance by a specific, desirable margin) and root causes forlow performance are mitigated (e.g., abandoning a specific brand ofpaint whose viscosity at the operating temperature of a painting tunnelresults in non-uniform coloration of painted products). Ranking oftools, group of tools, or conglomerate of tools is autonomous andproceeds in substantially the same manner as in a single autonomous toolsystem (e.g., system 360). Autonomous systems that support operation ofa conglomerate of autonomous tools considers such autonomousconglomerates as a single component regardless of the complexity of itsinternal structure, which can be accessed and managed through anautonomous system associated with the conglomerate.

FIG. 14 is a diagram 1400 that illustrates the modularity and recursivecoupling among classes of tools systems described above—e.g., individualautonomous tool 360, autonomous group tool 1200, and autonomousconglomerate tool 1300. In autonomous system 1400, goals, contexts, andassets circulate through knowledge network 375 which is depicted as anaxial gateway, and are conveyed to disparate autonomous tool systems360, 1200 and 1300. Such information and assets are acted upon in eachautonomous system, acts can include analysis, modification, generationof new information and assets; such acts are pictorially depicted as anarrow on the outer belt of each representation of autonomous systems360, 1200, 1300. Processed and generated assets are conveyed to theknowledge network 375, where can be circulated among autonomous system.In diagram 1400, processing and generation of assets is represented asoccurring azimuthally, whereas communication of assets is a radialprocess. As diagram 1400 depicts, autonomous tool systems are based onsubstantially the same elements that function in substantially the samemanner.

FIG. 15 illustrates an example system 1500 that assesses, and reportson, a multi-station process for asset generation. An autonomous system1505 that comprises an autonomous biologically based learning system360, an actor 390, and associated interaction component 330 can receiveand convey asset(s) 328 that originate in an N-station process 1510, andassess performance through backward chaining. The N-station process iseffected through a set of N process stations 1510 ₁-1510 _(N) thatproduce an output 1520 and can include individual autonomous tools 360,autonomous tool groups 1220, or autonomous tool conglomerates 1320. As aresult of performance assessment(s), autonomous system 1508 can locatetools, or group of tools, in process stations 1510 ₁-1510 _(N) withspecific degrees of performance degradation. In addition, for theselected station, autonomous system 1508 can provide an assessmentreport, a repair(s) report, or a maintenance schedule. It should beappreciated that disparate process stations can perform substantiallythe same operations; such a scenario would reflect the situation inwhich an output asset 1515 returns to a specific tool, or tool group,for further processing after the asset 1515 has been generated andtransported to a disparate tool, or group of tools, for furtherprocessing.

In backward chaining, action flow (e.g., process flow 1530) which leadsto an output typically counters a probe flow (e.g., assessment flow1540) which typically assesses the action flow. Thus, assessmentgenerally takes place in a top-bottom manner, in which assessment isconducted on a high-level stage of a specific action, e.g., a finalizedasset output 1520, and proceeds to lower-level stages in a quest tofocus the assessment on a specific stage prior to completion of aspecific action. As applied by autonomous system 1504, output asset 1520is received via process station N 1510 _(N). The autonomous system 1504can evaluate, as illustrated by 1546, a set of performance metrics{P_(N-1→N) ^((C))} leading to a specific degradation vector (not shown),based at least in part on a expected performance, for substantially alloperational components (e.g., tool, group or conglomerate tool) in theprocess station 1510 _(N). Additionally, it should be appreciated thatin process 1530, output assets (e.g., assets 1515) can be transportedacross disparate geographical areas, therefore the degradation vectorassessed by autonomous system 1504 can comprise metrics associated withthe in-transit portion of the process that leads to a partially finishedasset 1515. For example, when process 1530 regards accelerometers forvehicular airbag deployment, mechanical pieces in a transportedaccelerometer can be damaged as a consequence of utilizing analternative route for transporting the accelerometers rather thanemploying a route disclosed in the process 1530. When result(s) 1549 ofsuch an assessment indicate that N-station output 1520 is faulty,autonomous system 1504 isolates a faulty tool, or group of tools,associated with process station N, and generates a report (e.g.,assessment report 1550, repair(s) report 1560, or maintenance schedule1570). The generated report(s) can contain information to be utilized byone or more actors (e.g., actors 390 ₁-390 _(Q)). In addition, reportscan be stored to create a legacy of solutions (or “fixes”) for specificissues with performance, especially issues that appear infrequently sothat an actor's intervention can be preferred with respect to anautonomously developed solution which typically can benefit fromextensively available data. Moreover, availability of reports canfacilitate failure simulations or forensic analysis of a failureepisode, which can reduce manufacturing costs in at least two levels:(a) costly, infrequently failing equipment can be predicted to failunder rare conditions, which can be simulated by autonomous system 360,arising from operation of equipment by an actor with a backgroundnon-commensurate with the complexity of the equipment, (b) optimizationof parts inventory through prediction of various failure scenarios basedat least in part on historical data stored in assessment reports 1550and repair reports 1560.

In case results 1549 of process station N 1510 _(N) yield no faultytool, or group of tools, assessment is performed on a lower-levelprocess station N−1 1510 _(N-1) that generates a partially processedoutput asset 1515, and is a part in the process cycle 1530 to generateoutput 1520. Through analysis of a set of disparate performance metrics{P_(N-2→N-1) ^((C))}, a degree of degradation can be extracted andassociated tool, or group of tools (e.g., conglomerate C) can belocated. In instances that no faulty conglomerate of autonomous tools,or group of autonomous tools, or individual autonomous tool, autonomoussystem 1504 continues the backward, top-bottom assessment flow 1540 withthe object to locate sources of poor performance in final output 1520.

FIG. 16 is a block diagram of an example autonomous system 1600 whichcan distribute output assets that are autonomously generated by a toolconglomerate system. In system 1600, tool conglomerate 1320 _(Q) canautonomously generate a set of output assets 1610, which can be (i)information (e.g., structures and data patterns, relationships amongmeasured variables like a remedy to an existing degradation episode orcondition in alike or disparate tool groups that compose the autonomoustool conglomerate 1320 _(Q), and the like) gleaned or inferred about astate, including a performance degradation condition, of one or moretools that can compose tool conglomerate system 1320 _(Q); or (ii) anoutput product fabricated by said conglomerate. In addition, in system1600 output assets 1620 can be filtered by an asset selector 1620 andconveyed, or communicated, to a distribution component 1630. Suchdistribution component 1630 can exploit intelligent aspects ofautonomous biologically based learning system 360. The distributioncomponent 1630 comprises a management component 1635 that can manipulatea packaging component 1645 and an encryption component 1655 that canprepare the data, as well as a scheduler 1665 and an asset monitor 1675.Packaging component 1645 can prepare the asset to be distributed for adistribution process; such preparation can include damage prevention aswell lost prevention. For information (e.g., an event in episodic memory530 such as a system unwanted condition that develops as a result ofoperation outside a part specification like an temperature above athreshold) or data assets, packaging component 1645 can alter specificformats to present the information depending, at least partially, on theintended recipient of the asset to be distributed. For example,proprietary information can be abstract and presented withoutspecificity (e.g., explicit names of gases can be replaced with the word“gas;” relationships among specific parameters can be generalized to arelationship among variables such “p(O₂)<10⁻⁸ Torr” can be packaged as“p(gas)<10⁻⁸ Torr.”) In addition, packaging component 1645 can exploitan encryption component 1655 to ensure information integrity duringasset transmission and asset recovery at the intended recipient.

Additionally, in an aspect, management component 1635 can access (i) anasset store 1683, which typically contains assets scheduled to bedistributed or assets that have been distributed; (ii) a partner store1686 comprising commercial partners associated in the distribution orcompletion of specific assets; (iii) a customer store 1689 which cancontain current, past, or prospective customers to which the selectedasset has been, or can be distributed; (iv) a policy store that candetermine aspects associated to the distribution of assets, such aslicensing, customer support and relationships, procedures for assetpackaging, scheduling procedures, enforcement of intellectual propertyrights, and so on. It should be appreciated that information containedin policy store can change dynamically based at least in part onknowledge, e.g., information asset, learned or generated by autonomousbiologically based learning system.

Once an asset has been packaged, which can include adding to a package amonitoring device like RFID tags, either active or passive, or bar codes(e.g., two-dimensional codes, Aztec codes, etc.), and it has beenscheduled for distribution, a record of distribution can be stored, orif the asset is a data asset then a copy of the asset can be stored.Then, the asset can be delivered to a disparate autonomous toolconglomerate P 1320 _(P).

FIG. 17 illustrates an example of autonomously determined distributionsteps, from design to manufacturing and to marketing, for an asset(e.g., a finished product, a partially finished product, . . . ). Thehexagonal cell 1710 represents a specific geographic area (e.g., a city,a county, a state, one or more countries) wherein two classes ofautonomous tool conglomerates; e.g., “circular” conglomerates 1720,1730, 1740, 1750, and 1760, and “square” conglomerates 1765 and 1775,participate in the manufacturing chain of a set of products, or assets.(It is to be noted that the geographical area can encompasssubstantially any bound area in addition to a hexagonal cell.) As anexample scenario, and not by way of limitation, manufacturing of anasset starts at conglomerate 1720 which can be a conglomerate thatprovides design for custom-made solid state devices for opticalmanagement for high-mountain sports (e.g., skiing, climbing,paragliding, and so on). Design can consist in performing computationalsimulations of the optical properties of source materials and theircombinations, as well as device simulation. In such an instance,conglomerate 1720 can be a massively parallel supercomputer which can beconstrued in the subject example as a set of autonomous tool groups(FIG. 12), wherein each computer in the network of simulation computersis considered an autonomous tool group. Conglomerate 1720 outputs a oneor more designs of the optical device and a series of reports associatedwith description of the devices—e.g., a data asset. Such an output orasset (not shown), after appropriate encryption and packaging (e.g.,through component), can be conveyed to conglomerate 1730 via acommunication link 1724 which can be a wireless link.

Conglomerate 1730 can receive the data asset and, as a non-limitingexample, initiates a deposition process to fabricate a solid-statedevice according to the received asset. To that end, conglomerate 1730can partner with conglomerate 1740 and both can be regarded asfabrication facilities that are part of a two-conglomerate autonomousconglomerate tool 1310. Such conglomerates can produce multiple devicesaccording to the received specification asset, once a device isfabricated it can be tested, and assigned a quality and performancemetric, such metrics can lead to backward chaining to located “poorperformers” among the autonomous tools that enter conglomerates 1730 and1740. Through determination of multiple metrics, it is possible toautonomously adjust the operation of conglomerates 1720 and 1730 tooptimize production of the device, or output asset. It is noted thatlink 1724 indicates an internal link, wherein conglomerates 1730 and1740 are part of a same fabrication plant; thus the asset can betransported in substantially different conditions than when utilizinglink 1724 which provides a vehicular transportation route. Link 1744 canbe employed to ship devices for commercial packaging in a disparategeographic location (such transportation can be motivated byadvantageous packaging costs, skillful labor, corporate tax incentives,and so on). It should be appreciated that an autonomous learning systemat conglomerate 1740 can optimize the shipping times (via a scheduler,for example) and routes (e.g., link 1744) in order to ensure timely andcost effective delivery. At conglomerate 1750 assets are packed andremotely tested, via a wireless link, in conglomerate 1760. In anaspect, the volume of devices tested and the lots from which devices aretested can be determined by an autonomous system in conglomerate 1760.Once packed devices have been approved for commercialization, the assetsare shipped through road link 1744 at conglomerate 1740, andsubsequently shipped via road link 1770 to a disparate class ofconglomerate 1775. Such conglomerate can be a partner vendor, andconglomerate 1775 can be storage warehouse, which can be considered atool group conglomerate. Such conglomerate is linked, internally, toconglomerate 1765 which can be a showroom for the received assets.

In view of the example systems presented and described above, amethodology that may be implemented in accordance with the disclosedsubject matter, will be better appreciated with reference to theflowchart of FIGS. 18, 19, and 20. While, for purposes of simplicity ofexplanation, the methodologies are shown and described as a series ofblocks, it is to be understood and appreciated that the disclosedaspects are not limited by the number or order of acts, as some acts mayoccur in different orders and/or concurrently with other blocks fromwhat is depicted and described herein. Moreover, not all illustratedacts may be required to implement the methodologies describedhereinafter. It is to be appreciated that the functionality associatedwith the blocks may be implemented by software, hardware, a combinationthereof or any other suitable means (e.g., device, system, process,component). Additionally, it should be further appreciated that themethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such methodologies to various devices.Those skilled in the art will understand and appreciate that amethodology could alternatively be represented as a series ofinterrelated states or events, such as in a state diagram.

FIG. 18 presents a flowchart of an example method 1800 for biologicallybased autonomous learning with contextual goal adjustment. At act 1810 agoal is established. A goal is an abstraction associated with afunctionality of a goal component that is employed to accomplish thegoal or objective. A goal can be multi-disciplinary and span varioussectors (e.g., industrial, scientific, cultural, political, and so on).Generally act 1810 can be executed by an actor that can be external, orextrinsic, to a goal component that can be coupled to a learning system(e.g., adaptive inference engine). In view of the multidisciplinarynature of a goal, a goal component can be a tool, device, or system thatpossesses multiple functionalities; for instance, a tool system (e.g.,tool system 310) that performs a specific process, or a device thatprovides with a specific outcome to a set of requests, or the like. Atact 1820 data is received. Such data can be intrinsic, e.g., datagenerated in a goal component (e.g., component 120) that pursues a goal.In an aspect, as a part of performing the specific process, a set ofsensors or probes associated with the tool can gather the data that isreceived in an adaptive intelligent component. Received data can also beextrinsic, such as data conveyed by an actor (e.g., actor 190), whichcan be a human agent or a machine. Extrinsic data can be data that isutilized to drive a process or, generally, to drive an accomplishment ofa specific goal. A human agent can be an operator of the tool system,and can provide instructions or specific procedures associated with theprocesses performed by the tool. An example of an actor can be acomputer performing a simulation of the tool system, or substantiallyany goal component. It should be appreciated that simulation of the toolsystem can be employed to determine deployment parameters for the toolsystem, or for testing alternative conditions of operation for the tool(e.g., conditions of operations that can pose a hazard to a human agent,or can be costly). The received data can be training data or productiondata associated with a specific process, or generally a specific code.

In a further aspect, the received data can be associated with data typesor with procedural, or functional, units. A data type is a high levelabstraction of actual data; for instance, in an annealing state in thetool system a temperature can be controlled at a programmed level duringthe span of the annealing cycle, the time sequence of temperature valuesmeasured by a temperature sensor in the tool system can be associated asequence data type. Functional units can correspond to libraries ofreceived instructions, or processing code patches that manipulate datanecessary for the operation of the tool or for analyzing data generatedby the tool. Functional units can be abstracted into concepts related tothe specific functionality of the unit; for example, a multiplicationcode snippet can be abstracted into a multiply concept. Such conceptscan be overloaded, in that a single concept can be made dependent on aplurality of data types, such as multiply(sequence), multiply(matrix),or multiply(constant, matrix). Moreover, concepts associated withfunctional units can inherit other concepts associated with functionalunits, like derivative(scalar_product(vector, vector)) which canillustrate a concept that represents a derivative of a scalar product oftwo vectors with respect to an independent variable. It should beappreciated that functional concepts are in direct analogy with classes,which are in themselves concepts. Furthermore, data types can beassociated a priority and according to the priority can be deposited ina semantic network. Similarly, functional concepts (or autobots), canalso be associated with a priority, and deposited in a disparatesemantic network. Concept priorities are dynamic, and can facilitateconcept activation in the semantic networks.

At act 1830 knowledge is generated from the received data, which can berepresented in semantic networks, as discussed above. Generation ofknowledge can be accomplished by propagating activation in the semanticnetworks. Such propagation can be determined by a situation scoreassigned to a concept in addition to a score combination. In an aspect,score combination can be a weighted addition of two scores, or anaverage of two or more scores. It should be appreciated that a rule forscore combination can be modified as necessary, depending on tool systemconditions or information input received from an external actor. Itshould be appreciated that a priority can decay as time progresses toallow concepts that are seldom activated to became obsolete, allowingnew concepts to become more relevant.

The generated knowledge can be complete information; for instance, asteady-state pressure in a deposition step is a precise, well-definedmathematical function (e.g., a single-valued function with allparameters that enter the function deterministically assessed, ratherthan being stochastic or unknown) of two independent variables likesteady-state flow and steady state exhaust valve angle. Alternatively,the generated knowledge can represent a partial understanding; forexample, an etch rate can be possess a known functional dependence ontemperature (e.g., an exponential dependence), yet the specificrelationship—e.g., precise values of parameters that determine thefunctional dependence—between etch rate and temperature is unknown.

At act 1840 the generated knowledge is stored for subsequent utilizationof for autonomous generation of further knowledge. In an aspect,knowledge can be stored in a hierarchy of memories. A hierarchy can bedetermined on the persistence of knowledge in the memory and thereadability of knowledge for creation of additional knowledge. In anaspect, a third tier in the hierarchy can be an episodic memory (e.g.,episodic memory 530), wherein received data impressions and knowledgecan be collected. In such a memory tier manipulation of concepts is notsignificant, the memory acting instead as a reservoir of availableinformation received from a tool system or an external actor. In anaspect, such a memory can be identified as a metadatabase, in whichmultiple data types and procedural concepts can be stored. In a secondtier, knowledge can be stored in a short term memory wherein conceptscan be significantly manipulated and spread activation in semanticnetworks can take place. In such a memory tier, functional units orprocedural concepts operate on received data, and concepts to generatenew knowledge, or learning. A first tier memory can be a long termmemory (e.g., LTM 510) in which knowledge is maintained for activeutilization, with significant new knowledge stored in this memory tier.In addition, knowledge in a long term memory can be utilized byfunctional units in short term memory.

At act 1850 the generated or stored knowledge is utilized. Knowledge canbe employed to (i) determine a level of degradation of a goal component(e.g., tool system 310) by identifying differences between storedknowledge and newly received data (see self-awareness component 550),wherein the received data can be extrinsic (e.g., input 130) orintrinsic (e.g., a portion of output 140); (ii) characterize eitherextrinsic or intrinsic data or both, for example by identifying datapatterns or by discovering relationships among variables (such as in aself-conceptualization component 560), wherein the variables can beutilized to accomplish the established goal; or (iii) generate ananalysis of the performance of the tool system that generates the data(e.g., self-optimization component 570), providing indications of rootcause for predicted failures or existing failures as well as necessaryrepairs or triggering alarms for implementing preventive maintenancebefore degradation of the tool system causes tool failure. It is to benoted that utilization of the stored and generated knowledge is affectedby the received data—extrinsic or intrinsic—and the ensuing generatedknowledge.

Act 1860 is a validation act in which the degree of accomplishment of agoal can be inspected in view of generated knowledge. In case theestablished goal is accomplished, example method 1800 can end.Alternatively, if the established goal has not been accomplished, theestablished goal can be reviewed at act 1870. In the latter, flow ofmethod 1800 can lead to establishing a new goal in case a current goalis to be revised or adapted; for instance, goal adaptation can be basedon generated knowledge. In case no revision of a current goal is to bepursued, flow of method 1800 is returned to generate knowledge, whichcan be utilized to continue pursuing the currently established goal.

FIG. 19 presents a flowchart 1900 of an example method for adjusting asituation score of a concept associated with a state of a goalcomponent. At act 1910 a state of a goal component is determined. Astate typically is established through a context, which can bedetermined by various data input (e.g., input 130), or through a networkof concepts associated with the input and exhibiting specificrelationships. The input data relates to a goal that is pursued by thegoal component; for instance, a recipe for a coating process of aspecific thin-film device can be deemed as input associated with a“deposit an insulating device” goal. At act 1920 a set of concepts thatcan be applied to the state of the goal component is determined. Suchconcepts can be abstractions of data types entered in act 1910, or canbe existing concepts in a memory platform (e.g., long term memory 510,or short term memory 520). Generally, functional concepts that can acton descriptive concepts (e.g., concepts with no functional component)can be utilized more frequently towards achieving a goal. At act 1930 asituation score for each concept in a set of concepts associated withthe goal state is determined. A set of situation scores can establish ahierarchy for concept utilization or application, which can determinethe dynamics of a goal, like goal adaptation or sub-goalcreation/randomization. Adjustment of situation scores for specificconcepts can drive goal accomplishment as well as propagation within aspace of goals as part of goal adaptation.

FIG. 20 presents a flowchart 2000 of an example method for generatingknowledge through inference. At act 2010 a concept is associated to adata type and a priority for the concept is determined. Prioritiestypically can be determined based on a probability of utilization of aconcept, or a concept's weight. Such a weight can be determined througha function (e.g., a weighted sum, or a geometric average) of parametersthat can represent the ease to utilize a concept (e.g., the complexityto operate on a data type), such a parameter can be identified with aconcept's inertia, and the suitability parameter of a concept todescribe a state (e.g., a number of neighboring concepts that can berelated the concept). It should be appreciated that a priority can betime dependent as a consequence of explicitly time-dependent inertia andsuitability parameters, or as a result of concept propagation. Timedependent priorities can introduce aging aspects into specific conceptsand thus can promote knowledge flexibility (e.g., knowledge (forexample, a paradigm employed to pursue a goal, such as a recipe forpreparation of a nano-structured device) through concepts ceasing to berelevant in a particular knowledge scenario (e.g., node structure in apriority-based knowledge network). At act 2020 a semantic network for aset of prioritized concepts is established. It should be appreciatedthat the semantic network can comprise multiple sub-networks, whereineach of the multiple networks can characterize a set of relationshipsamong concepts in a class. As an example, in a two-tier semanticnetwork, a first sub-network can represent relationships among conceptsderived from data types, whereas a second sub-network can compriserelationships among functional concepts (e.g., a planner autobot orüberbot, a conceptual autobot) describing operations that can beutilized to alter upon a data type. At act 2030 the set of priorities ispropagated over the semantic network to make an inference and thusgenerate knowledge associated with the network of concepts. In anaspect, such propagation can be utilized to generate optimization plansfor goal adaptation, or to predict failures in a system that pursues aspecific goal.

FIG. 21 is a flowchart of an example method 2100 for asset distribution.Asset(s) can be provided by an individual autonomous tool, an autonomousgroup tool (e.g., system 1210), or an autonomous conglomerated toolsystem (e.g., system 1310). It should be appreciated that assets can begenerated in alternative manners as well. At act 2110 an asset isreceived. In an aspect, the received asset can be an asset selected fromoutput asset(s) generated by one or more autonomous tools. At act 2120the received asset is processed for distribution. As discussed above, anasset typically carries advantages associated with knowledge utilized ingenerating the asset; thus, an asset can be packaged in such a mannerthat prevent a competitor to reverse-engineer the asset. It should beappreciated that depending on the destination of the asset, packaginginformation associated to the asset can be customized, deliveringdisparate levels of information based at least in part on whether theentity that receives the asset is a commercial partner, or a customer,or other branch, division, or group of an organization that fabricatesthe asset. The level of information packaged with the asset can followspecific policies (for example, policies stored in policy store 1692).Additionally, for data assets or computer program assets, such assetscan be encrypted while being packaged in order retain integrity of theinformation conveyed by the asset. Moreover, part of the processing fordistributing an asset can include retaining the asset in storage (e.g.,asset store 1683) while a suitable distribution schedule is followed. Inan aspect, such schedule can be optimized by an autonomous system (e.g.,system 360) that supports a tools system the fabricates, or produces,the asset to be distributed.

At act 2130 the processed asset is distributed. Distribution typicallydepends on the asset features and characteristics, as well as on thedestination of the asset. For example, assets can be distributed withina factory plant, in order to complete asset production like in anassembly line wherein an unfinished vehicle (e.g., an asset) can betransported through different stages of assembly. Similarly, in the foodindustry, a frozen meal (e.g., asset) is distributed throughout a foodpreparation plant. Alternatively, or in addition, depending on industry,an unfinished asset can be distributed to overseas to be finished inorder to benefit from cost-effective production markets.

At act 2140, an distributed asset is monitored in order to ensure, forexample, the asset distribution adheres to applicable distributionregulation, or to ensure adequate inventory replenishment by havingaccess to distribution status of the asset. In addition, monitoringdistribution of the asset can mitigate losses and damages, as well ascan facilitate interaction with commercial partners and customers.

Various aspects or features described herein may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ],smart cards, and flash memory devices (e.g., card, stick, key drive . .. ).

What has been described above includes examples of the claimed subjectmatter. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe claimed subject matter, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the claimedsubject matter are possible. Accordingly, the claimed subject matter isintended to embrace all such alterations, modifications and variationsthat fall within the spirit and scope of the appended claims.Furthermore, to the extent that the term “includes” is used in eitherthe detailed description or the claims, such term is intended to beinclusive in a manner similar to the term “comprising” as “comprising”is interpreted when employed as a transitional word in a claim.

1. A semiconductor tool system comprising: a set of tools to fabricate aproduct, each tool in the set of tools generates a product asset thatfacilitates fabrication of the product; a set of autonomous learningsystems, each autonomous learning system resides in each tool in the setof tools and optimizes performance of each tool; and a disparateautonomous learning system that receives packaged data from the set oftools and generates a knowledge that optimizes the fabrication of theproduct.
 2. The system of claim 1, wherein optimized fabrication of theproduct comprises an optimized mean time between failures of the set oftools.
 3. The system of claim 1, wherein optimized fabrication of theproduct comprises an optimized mean time to repair.
 4. The system ofclaim 1, wherein optimized fabrication of the product comprises afabrication process optimized through backward chaining.
 5. The systemof claim 4, wherein backward chaining for a product degradation includesranking each tool in a set of tools according to product degradation. 6.The system of claim 5, wherein optimized fabrication of the productcomprises learning to predict a failure of a set of parts.
 7. The systemof claim 6, further comprising utilizing an expected time to failure tooptimize a parts inventory.
 8. The system of claim 1, wherein theoptimized fabrication of the product comprises learning to predict a setof factors for ramp-up.
 9. The system of claim 7, further comprisingoptimizing the set of factors for ramp-up.
 10. The system of claim 1,each autonomous learning system comprises a memory platform that storesthe knowledge, wherein the memory platform includes a hierarchy ofmemories.
 11. The system of claim 1, each autonomous learning systemcomprises a platform that processes data, wherein the platform includesa set of functional units that operate on the received packaged data.12. The system of claim 11, wherein a knowledge network facilitatescommunication among the hierarchy of memories in the memory platform,the set of functional units in the platform that processes the packageddata, or a combination thereof.
 13. The system of claim 10, thehierarchy of memories includes a long term memory, a short term memory,and an episodic memory.
 14. The system of claim 11, the set offunctional units includes a memory platform and a platform thatprocesses data.
 15. The system of claim 11, the set of functional unitsincludes at least one unit that identifies a degradation condition ofeach tool in the set of tools.
 16. The system of claim 15, wherein thedegradation condition evaluates differences between a set of features infirst fabrication process and a second fabrication process.
 17. Thesystem of claim 15, wherein a degradation condition includes a singlenumeric value, the numeric value assesses a distance amongst two vectorsof N tuples, wherein each tuple in each vector is a computed metric ofobserved data and further N is a positive integer
 18. The system ofclaim 17, the numeric positive value is a Euclidean distance.
 19. Thesystem of claim 17, the numeric value includes a cosine similaritymetric.
 20. The system of claim 1, wherein a product asset comprises arecipe for at least a portion of a process to fabricate a product. 21.The system of claim 1, wherein a product asset comprises a set ofobserved data associated with measurements conducted on a product. 22.The system of claim 21, the observed data includes at least one of aindex of refraction, an absorption coefficients, a developmentinspection critical dimension, or a final inspection critical dimension.23. The system of claim 21, wherein a product asset further comprises aset of computed metrics of the observed data
 24. The system of claim 23,wherein the set of metrics includes at least one of a mean value, astandard deviation value, or a uniformity value.
 25. The system of claim1, wherein a product asset comprises an at least partially processedsolid state semiconductor material.
 26. A method for distributing aproduct asset; the method comprising: receiving a product asset,processing the product asset based at least in part on knowledgegenerated by an autonomous learning system; and distributing theprocessed product asset.
 27. The method of claim 26, further comprisingmonitoring the distributed product asset.
 28. The method of claim 26,processing the product asset based at least in part on knowledgegenerated by an autonomous learning system further comprising packagingthe product asset.
 29. The method of claim 28, packaging the productasset comprises encrypting a set of data.
 30. The method of claim 26,processing the product asset based at least in part on knowledgegenerated by an autonomous learning system further comprising schedulinga distribution time for the product asset.
 31. An apparatus forsemiconductor processing, comprising: means for fabricating a product;means for optimizing performance of a tool system; means for receivingpackaged data from a set of tools; and means for generating a knowledgethat optimizes the fabrication of a product.